from reportlab.lib.pagesizes import A4
from reportlab.lib import colors
from reportlab.lib.units import cm, mm
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.enums import TA_LEFT, TA_CENTER, TA_JUSTIFY
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, PageBreak, KeepTogether
)
from reportlab.platypus.flowables import BalancedColumns
from reportlab.lib.colors import HexColor
import os

OUTPUT_PATH = "/tmp/workspace/ortho-pg-pdf/Pagets_Ewings_Ortho_PG.pdf"

# ─── Color Palette ────────────────────────────────────────────────────────────
DARK_BLUE   = HexColor("#1A3A5C")   # headings
MID_BLUE    = HexColor("#2B6CB0")   # sub-headings
TEAL        = HexColor("#2C7A7B")   # section accent
GOLD        = HexColor("#D69E2E")   # highlight / exam tips
LIGHT_BLUE  = HexColor("#EBF4FF")   # table header bg
LIGHT_GREY  = HexColor("#F7FAFC")   # alt row
RED_ACCENT  = HexColor("#C53030")   # warnings / important
GREEN_ACCENT= HexColor("#276749")   # prognosis / good outcomes
WHITE       = colors.white
BLACK       = colors.black

# ─── Document ─────────────────────────────────────────────────────────────────
doc = SimpleDocTemplate(
    OUTPUT_PATH,
    pagesize=A4,
    rightMargin=2*cm, leftMargin=2*cm,
    topMargin=2.5*cm, bottomMargin=2.5*cm,
    title="Paget's Disease & Ewing's Sarcoma – Ortho PG Notes",
    author="Orris Medical Education"
)

W, H = A4
TEXT_W = W - 4*cm

# ─── Styles ───────────────────────────────────────────────────────────────────
base = getSampleStyleSheet()

def style(name, parent="Normal", **kw):
    s = ParagraphStyle(name, parent=base[parent], **kw)
    return s

S_cover_title   = style("CoverTitle",   fontSize=28, textColor=WHITE,
                        fontName="Helvetica-Bold", leading=34, alignment=TA_CENTER)
S_cover_sub     = style("CoverSub",     fontSize=16, textColor=HexColor("#BEE3F8"),
                        fontName="Helvetica", leading=22, alignment=TA_CENTER)
S_cover_tag     = style("CoverTag",     fontSize=11, textColor=HexColor("#FED7AA"),
                        fontName="Helvetica-Oblique", alignment=TA_CENTER)

S_chapter       = style("Chapter",      fontSize=20, textColor=WHITE,
                        fontName="Helvetica-Bold", leading=26, alignment=TA_CENTER,
                        spaceAfter=4)
S_h1            = style("H1",           fontSize=16, textColor=DARK_BLUE,
                        fontName="Helvetica-Bold", leading=20,
                        spaceBefore=12, spaceAfter=4)
S_h2            = style("H2",           fontSize=13, textColor=MID_BLUE,
                        fontName="Helvetica-Bold", leading=17,
                        spaceBefore=8, spaceAfter=3)
S_h3            = style("H3",           fontSize=11, textColor=TEAL,
                        fontName="Helvetica-Bold", leading=14,
                        spaceBefore=6, spaceAfter=2)
S_body          = style("Body",         fontSize=10, textColor=BLACK,
                        fontName="Helvetica", leading=14, alignment=TA_JUSTIFY,
                        spaceAfter=4)
S_bullet        = style("Bullet",       fontSize=10, textColor=BLACK,
                        fontName="Helvetica", leading=14, leftIndent=14,
                        bulletIndent=4, spaceAfter=2,
                        bulletText="\u2022")
S_bullet2       = style("Bullet2",      fontSize=10, textColor=BLACK,
                        fontName="Helvetica", leading=13, leftIndent=28,
                        bulletIndent=14, spaceAfter=1,
                        bulletText="\u25e6")
S_exam_tip      = style("ExamTip",      fontSize=10, textColor=HexColor("#7B341E"),
                        fontName="Helvetica-Bold", leading=14, leftIndent=10,
                        rightIndent=10, spaceAfter=4)
S_important     = style("Important",    fontSize=10, textColor=RED_ACCENT,
                        fontName="Helvetica-Bold", leading=14, leftIndent=10)
S_table_hdr     = style("TblHdr",       fontSize=9,  textColor=WHITE,
                        fontName="Helvetica-Bold", leading=12, alignment=TA_CENTER)
S_table_body    = style("TblBody",      fontSize=9,  textColor=BLACK,
                        fontName="Helvetica", leading=12, alignment=TA_LEFT)
S_table_body_c  = style("TblBodyC",     fontSize=9,  textColor=BLACK,
                        fontName="Helvetica", leading=12, alignment=TA_CENTER)
S_caption       = style("Caption",      fontSize=8,  textColor=HexColor("#4A5568"),
                        fontName="Helvetica-Oblique", leading=11, alignment=TA_CENTER,
                        spaceAfter=4)
S_footer_note   = style("FootNote",     fontSize=8,  textColor=HexColor("#718096"),
                        fontName="Helvetica-Oblique", leading=11, alignment=TA_CENTER)

# ─── Helper Flowables ─────────────────────────────────────────────────────────
def hr(color=MID_BLUE, width=1):
    return HRFlowable(width="100%", thickness=width, color=color, spaceAfter=4, spaceBefore=4)

def chapter_banner(text, color=DARK_BLUE):
    tbl = Table([[Paragraph(text, S_chapter)]], colWidths=[TEXT_W])
    tbl.setStyle(TableStyle([
        ("BACKGROUND",  (0,0), (-1,-1), color),
        ("TOPPADDING",  (0,0), (-1,-1), 14),
        ("BOTTOMPADDING",(0,0),(-1,-1), 14),
        ("LEFTPADDING", (0,0), (-1,-1), 10),
        ("RIGHTPADDING",(0,0), (-1,-1), 10),
        ("ROUNDEDCORNERS", [8]),
    ]))
    return tbl

def section_box(title, content_para, bg=LIGHT_BLUE, border=MID_BLUE):
    """A bordered coloured box for highlighted content."""
    rows = [[Paragraph(title, S_h3)], [content_para]]
    tbl = Table(rows, colWidths=[TEXT_W - 10])
    tbl.setStyle(TableStyle([
        ("BACKGROUND",  (0,0), (-1,0), bg),
        ("BACKGROUND",  (0,1), (-1,-1), HexColor("#FEFEFE")),
        ("BOX",         (0,0), (-1,-1), 1.2, border),
        ("LINEBELOW",   (0,0), (-1,0), 1, border),
        ("TOPPADDING",  (0,0), (-1,-1), 6),
        ("BOTTOMPADDING",(0,0),(-1,-1), 6),
        ("LEFTPADDING", (0,0), (-1,-1), 8),
        ("RIGHTPADDING",(0,0), (-1,-1), 8),
    ]))
    return tbl

def exam_box(text):
    tbl = Table([[Paragraph("★  EXAM TIP: " + text, S_exam_tip)]], colWidths=[TEXT_W])
    tbl.setStyle(TableStyle([
        ("BACKGROUND",  (0,0), (-1,-1), HexColor("#FFFBEB")),
        ("BOX",         (0,0), (-1,-1), 1.5, GOLD),
        ("TOPPADDING",  (0,0), (-1,-1), 6),
        ("BOTTOMPADDING",(0,0),(-1,-1), 6),
        ("LEFTPADDING", (0,0), (-1,-1), 10),
        ("RIGHTPADDING",(0,0), (-1,-1), 10),
    ]))
    return tbl

def warning_box(text):
    tbl = Table([[Paragraph("⚠  " + text, S_important)]], colWidths=[TEXT_W])
    tbl.setStyle(TableStyle([
        ("BACKGROUND",  (0,0), (-1,-1), HexColor("#FFF5F5")),
        ("BOX",         (0,0), (-1,-1), 1.5, RED_ACCENT),
        ("TOPPADDING",  (0,0), (-1,-1), 6),
        ("BOTTOMPADDING",(0,0),(-1,-1), 6),
        ("LEFTPADDING", (0,0), (-1,-1), 10),
        ("RIGHTPADDING",(0,0), (-1,-1), 10),
    ]))
    return tbl

def b(text, para=None):
    """Bullet paragraph"""
    p = para or S_bullet
    return Paragraph(text, p)

def h1(t): return Paragraph(t, S_h1)
def h2(t): return Paragraph(t, S_h2)
def h3(t): return Paragraph(t, S_h3)
def p(t):  return Paragraph(t, S_body)
def sp(n=6): return Spacer(1, n)

def simple_table(header_row, data_rows, col_widths=None, alt=True):
    """Create a styled table."""
    all_rows = [[Paragraph(str(c), S_table_hdr) for c in header_row]]
    for row in data_rows:
        all_rows.append([Paragraph(str(c), S_table_body) for c in row])
    col_w = col_widths or [TEXT_W / len(header_row)] * len(header_row)
    tbl = Table(all_rows, colWidths=col_w, repeatRows=1)
    style_cmds = [
        ("BACKGROUND",   (0,0), (-1,0),  DARK_BLUE),
        ("ROWBACKGROUNDS",(0,1),(-1,-1), [WHITE, LIGHT_GREY] if alt else [WHITE]),
        ("BOX",          (0,0), (-1,-1), 0.8, MID_BLUE),
        ("INNERGRID",    (0,0), (-1,-1), 0.5, HexColor("#BEE3F8")),
        ("VALIGN",       (0,0), (-1,-1), "TOP"),
        ("TOPPADDING",   (0,0), (-1,-1), 5),
        ("BOTTOMPADDING",(0,0), (-1,-1), 5),
        ("LEFTPADDING",  (0,0), (-1,-1), 6),
        ("RIGHTPADDING", (0,0), (-1,-1), 6),
    ]
    tbl.setStyle(TableStyle(style_cmds))
    return tbl

# ═══════════════════════════════════════════════════════════════════════════════
# BUILD CONTENT
# ═══════════════════════════════════════════════════════════════════════════════
story = []

# ─────────────────────────── COVER PAGE ──────────────────────────────────────
cover_bg = Table(
    [[Paragraph("ORTHOPAEDICS PG EXAMINATION NOTES", S_cover_sub)],
     [Spacer(1, 10)],
     [Paragraph("Paget's Disease of Bone", S_cover_title)],
     [Paragraph("&", style("Amp", fontSize=22, textColor=HexColor("#90CDF4"),
                               fontName="Helvetica-Bold", alignment=TA_CENTER))],
     [Paragraph("Ewing's Sarcoma", S_cover_title)],
     [Spacer(1, 20)],
     [Paragraph("With Emphasis on Management | Based on Campbell's, Harrison's, Rockwood & Green's", S_cover_tag)],
     [Spacer(1, 8)],
     [Paragraph("July 2026", S_cover_tag)],
    ],
    colWidths=[TEXT_W]
)
cover_bg.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,-1), DARK_BLUE),
    ("TOPPADDING",   (0,0), (-1,-1), 14),
    ("BOTTOMPADDING",(0,0), (-1,-1), 14),
    ("LEFTPADDING",  (0,0), (-1,-1), 30),
    ("RIGHTPADDING", (0,0), (-1,-1), 30),
    ("BOX",          (0,0), (-1,-1), 3, GOLD),
]))
story.append(cover_bg)
story.append(Spacer(1, 1*cm))
story.append(Paragraph(
    "Sources: Campbell's Operative Orthopaedics 15th Ed 2026 | Harrison's 22E 2025 | "
    "Rockwood & Green's 10th Ed 2025 | Miller's Review of Orthopaedics 9th Ed | "
    "Katzung Pharmacology 16th Ed | Firestein & Kelley's Rheumatology",
    S_footer_note))
story.append(PageBreak())

# ──────────────────────────── TABLE OF CONTENTS ───────────────────────────────
story.append(h1("Table of Contents"))
story.append(hr())
toc_data = [
    ["Section", "Topic", "Page"],
    ["PART 1", "Paget's Disease of Bone", "3"],
    ["", "1.1  Epidemiology & Aetiology", "3"],
    ["", "1.2  Pathophysiology", "3"],
    ["", "1.3  Clinical Features", "4"],
    ["", "1.4  Investigations & Diagnosis", "4"],
    ["", "1.5  Complications", "5"],
    ["", "1.6  Management (Emphasis)", "5"],
    ["", "1.7  Orthopaedic Surgical Management", "7"],
    ["", "1.8  Prognosis & Follow-up", "7"],
    ["", "1.9  High-Yield Summary Table", "8"],
    ["PART 2", "Ewing's Sarcoma", "9"],
    ["", "2.1  Epidemiology & Molecular Biology", "9"],
    ["", "2.2  Pathology & Classification", "9"],
    ["", "2.3  Clinical Features", "10"],
    ["", "2.4  Imaging", "10"],
    ["", "2.5  Staging (Enneking)", "11"],
    ["", "2.6  Management (Emphasis)", "11"],
    ["", "2.7  Radiation Therapy", "13"],
    ["", "2.8  Prognosis & Prognostic Factors", "13"],
    ["", "2.9  Recurrence & Salvage", "14"],
    ["", "2.10 High-Yield Summary Table", "14"],
    ["PART 3", "Quick Comparison Table", "15"],
]
toc_col_w = [2.5*cm, 10*cm, 2.5*cm]
toc_rows = [[Paragraph(r[0], S_table_body_c),
             Paragraph(r[1], S_table_body),
             Paragraph(r[2], S_table_body_c)] for r in toc_data[1:]]
toc_hdr  = [Paragraph(c, S_table_hdr) for c in toc_data[0]]
toc_tbl  = Table([toc_hdr] + toc_rows, colWidths=toc_col_w)
toc_tbl.setStyle(TableStyle([
    ("BACKGROUND",   (0,0),(-1,0), DARK_BLUE),
    ("ROWBACKGROUNDS",(0,1),(-1,-1),[WHITE, LIGHT_GREY]),
    ("BOX",          (0,0),(-1,-1), 0.8, MID_BLUE),
    ("INNERGRID",    (0,0),(-1,-1), 0.4, HexColor("#BEE3F8")),
    ("TOPPADDING",   (0,0),(-1,-1), 4),
    ("BOTTOMPADDING",(0,0),(-1,-1), 4),
    ("LEFTPADDING",  (0,0),(-1,-1), 6),
    ("RIGHTPADDING", (0,0),(-1,-1), 6),
    ("VALIGN",       (0,0),(-1,-1),"MIDDLE"),
]))
story.append(toc_tbl)
story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# PART 1  ──  PAGET'S DISEASE OF BONE
# ═══════════════════════════════════════════════════════════════════════════════
story.append(chapter_banner("PART 1: PAGET'S DISEASE OF BONE", DARK_BLUE))
story.append(sp(10))

# 1.1 EPIDEMIOLOGY & AETIOLOGY
story.append(h1("1.1  Epidemiology & Aetiology"))
story.append(hr())
story.append(p(
    "Paget's disease (osteitis deformans) is the second most common metabolic bone disease after osteoporosis. "
    "It is a <b>localised bone-remodeling disorder</b> affecting non-contiguous areas of the skeleton, "
    "characterised by overactive osteoclastic resorption followed by disorganised osteoblastic new bone formation. "
    "The resulting mosaic of woven and lamellar bone is expanded, less compact, more vascular, and prone to "
    "deformity and fracture."
))
cols_epi = [
    [b("<b>Prevalence:</b> ~3% autopsy series over age 40"),
     b("<b>Sex:</b> Males > Females"),
     b("<b>Age:</b> Rarely before 40; rises sharply with age"),
     b("<b>Geography:</b> UK, France, Germany, Australia, NZ, S. Africa; rare in Asia, Africa, Americas (native)"),
     b("<b>Trend:</b> Declining prevalence and severity over recent decades"),
    ],
    [b("<b>Positive family history:</b> 15-25% of patients; 7-10× risk in 1st-degree relatives"),
     b("<b>TNFRSF11B gene</b> (OPG): juvenile Paget's / familial idiopathic hyperphosphatasia"),
     b("<b>TNFRSF11A gene</b> (RANK): familial expansile osteolysis, early-onset Paget's"),
     b("<b>Paramyxovirus</b> (measles, respiratory syncytial): intranuclear inclusions in osteoclasts"),
     b("<b>VCP gene</b> mutation: Inclusion body myopathy + Paget's + frontotemporal dementia"),
    ]
]
epi_tbl = Table([cols_epi], colWidths=[TEXT_W/2, TEXT_W/2])
epi_tbl.setStyle(TableStyle([("VALIGN",(0,0),(-1,-1),"TOP"), ("LEFTPADDING",(0,0),(-1,-1),0)]))
story.append(epi_tbl)
story.append(sp(6))
story.append(exam_box(
    "Paget's is the SECOND most common metabolic bone disease. "
    "RANK/OPG pathway mutations are key genetics to know. "
    "Paramyxoviral etiology is debated but frequently asked."
))

# 1.2 PATHOPHYSIOLOGY
story.append(sp(8))
story.append(h1("1.2  Pathophysiology"))
story.append(hr())
story.append(p("Three histological phases (may coexist in different areas of same bone):"))
phases_data = [
    ["Phase", "Predominant Activity", "Histology / Feature", "Radiograph"],
    ["1. Osteolytic\n(Hot/Active)", "Osteoclastic resorption", "Giant multinucleated osteoclasts; "
     "\"Osteoporosis circumscripta\" in skull\n\"Blade of grass\" in long bones", "Lytic lesion advancing"],
    ["2. Mixed\n(Active)", "Both active", "Mosaic woven + lamellar bone\n\"Mosaic pattern\" cement lines",
     "Mixed lytic-sclerotic"],
    ["3. Osteosclerotic\n(Burnt-out/Quiescent)", "Osteoblastic formation dominant",
     "Dense, disorganised sclerotic bone\nMarrow replaced by fibrovascular tissue",
     "Dense, enlarged, deformed bone"],
]
story.append(simple_table(
    phases_data[0], phases_data[1:],
    col_widths=[3*cm, 3.5*cm, 5.5*cm, 3*cm]
))
story.append(sp(6))
story.append(p(
    "The coupling of osteoclast and osteoblast activity leads to <b>elevated Alkaline Phosphatase (ALP)</b> "
    "– the hallmark biochemical marker. Pagetic bone is hypervascular, leading to increased local blood flow."
))
story.append(exam_box(
    "Classic ALP elevation in Paget's with NORMAL serum calcium and phosphate. "
    "\"Mosaic pattern\" or \"jigsaw puzzle\" cement lines are pathognomonic on histology."
))

# 1.3 CLINICAL FEATURES
story.append(sp(8))
story.append(h1("1.3  Clinical Features"))
story.append(hr())
story.append(p(
    "<b>Most patients (up to 70%) are asymptomatic</b>, diagnosed incidentally. "
    "Symptomatic disease depends on site involved."
))
cf_data = [
    ["System / Site", "Feature"],
    ["Bone pain (most common)", "Dull, aching, deep, worse at rest and night; from vascularity, lytic lesions, "
     "microfractures"],
    ["Long bones (femur, tibia)", "Bowing deformity (sabre tibia), warmth; fractures "
     "(\"chalk stick\" or transverse) – femoral shaft, subtrochanteric region"],
    ["Skull (most common site)", "Enlarged hat size, frontal bossing, cranial nerve palsies, "
     "hearing loss (cochlear nerve), platybasia, brainstem compression"],
    ["Vertebrae", "Back pain, spinal stenosis, nerve root compression, \"ivory vertebra\" / "
     "\"picture frame\" vertebra, rarely cord compression (vascular steal)"],
    ["Pelvis", "Brim sign (thickened iliopectineal line), protrusio acetabuli, secondary OA hip"],
    ["Face", "Facial deformity, dental problems, lion face (leontiasis ossea in severe cases)"],
    ["Cardiovascular", "High-output cardiac failure (>15-35% skeleton involved); AV shunting; "
     "calcific aortic stenosis"],
    ["Neurological", "Hearing loss most common; cranial nerve palsies; compression syndromes"],
]
story.append(simple_table(
    cf_data[0], cf_data[1:],
    col_widths=[4.5*cm, 10.5*cm]
))

# 1.4 INVESTIGATIONS
story.append(sp(8))
story.append(h1("1.4  Investigations & Diagnosis"))
story.append(hr())

inv_data = [
    ["Investigation", "Finding / Significance"],
    ["Serum ALP (TOTAL)", "ELEVATED – best single marker; reflects disease activity and extent; "
     "used to monitor therapy response. First-line test."],
    ["PINP (bone formation marker)", "Elevated; useful when ALP normal (single-site disease). "
     "Can be used in place of ALP."],
    ["Serum N-/C-telopeptide", "Elevated bone resorption markers; fall faster than ALP with treatment. "
     "Useful early treatment monitor."],
    ["Serum Ca, PO4", "Usually NORMAL. Hypercalcemia may occur with immobilization."],
    ["Urine Ca/creatinine", "Elevated in active disease; monitor renal complications."],
    ["Bone-specific ALP", "Useful when single-site disease with normal total ALP"],
    ["Serum osteocalcin", "NOT reliable – not recommended for diagnosis or monitoring"],
]
story.append(simple_table(inv_data[0], inv_data[1:], col_widths=[4.5*cm, 10.5*cm]))
story.append(sp(6))

story.append(h2("Radiology"))
story.append(p("<b>Plain Radiograph (X-ray):</b> Often diagnostic. Key features:"))
xray_feats = [
    "<b>Skull:</b> \"Cotton wool\" appearance (mixed lytic-sclerotic), osteoporosis circumscripta, "
    "diploic thickening, frontal bossing",
    "<b>Vertebrae:</b> \"Picture frame\" vertebra (cortical thickening of end plates), \"ivory vertebra\" "
    "(diffuse sclerosis)",
    "<b>Pelvis:</b> Brim sign (thickened iliopectineal line), protrusio acetabuli, coarse trabeculation",
    "<b>Long bones:</b> Expansion, cortical thickening, \"blade of grass\" advancing lytic lesion, "
    "bowing deformity, \"chalk stick\" fracture (transverse fracture in convexity)",
    "<b>Femur proximal:</b> \"Shepherd's crook\" deformity from coxa vara",
]
for f in xray_feats:
    story.append(b(f))
story.append(sp(4))
story.append(p(
    "<b>Bone Scintigraphy (Tc-99m HDP bone scan):</b> Most sensitive for detecting extent and activity; "
    "shows intense uptake in affected areas. Best for <b>mapping disease extent</b>. "
    "\"Bull's-eye\" sign in skull."
))
story.append(p(
    "<b>CT:</b> Assesses cortical thickness, bony deformity, complication planning. "
    "<b>MRI:</b> Required to exclude sarcomatous transformation, assess spinal cord/nerve root compression."
))
story.append(exam_box(
    "Bone scan = most sensitive for extent. X-ray = most specific for diagnosis. "
    "ALP = best marker for monitoring. Serum Ca is NORMAL (contrast with hyperparathyroidism)."
))

# 1.5 COMPLICATIONS
story.append(sp(8))
story.append(h1("1.5  Complications"))
story.append(hr())
comp_data = [
    ["Complication", "Details"],
    ["Fractures", "\"Chalk-stick\" (transverse) fractures in bowed long bones; femoral shaft, "
     "subtrochanteric; occur in lytic phase; can be pathological"],
    ["Sarcomatous transformation", "<b>&lt;0.5% of cases</b> but the most feared complication. "
     "Usually osteosarcoma (&gt;50%). Also fibrosarcoma, chondrosarcoma. "
     "Suspect: new/worsening pain + soft tissue mass. Very poor prognosis."],
    ["Secondary osteoarthritis", "Adjacent joint (hip, knee) due to deformity and altered biomechanics; "
     "most common complication requiring orthopaedic intervention"],
    ["Nerve compression", "Hearing loss (VIII), cranial nerve palsies, spinal stenosis, "
     "cord compression, radiculopathy"],
    ["High-output cardiac failure", "Extensive disease (>15-35% skeleton); AV shunting; rare"],
    ["Hypercalcemia", "Immobilization of active Paget's; check calcium before immobilizing"],
    ["Benign giant cell tumor", "Adjacent to pagetic bone; responds to glucocorticoids"],
]
story.append(simple_table(comp_data[0], comp_data[1:], col_widths=[4.5*cm, 10.5*cm]))
story.append(sp(4))
story.append(warning_box(
    "Sarcomatous transformation (&lt;0.5%): presents as new pain in long-standing Paget's. "
    "5-year survival &lt;10%. Any sudden worsening pain = MRI urgently to exclude sarcoma."
))

# 1.6 MANAGEMENT ─── EMPHASIS
story.append(sp(8))
story.append(h1("1.6  Management of Paget's Disease"))
story.append(hr(color=GOLD, width=2))
story.append(p("<b>Indications for Treatment (Endocrine Society 2014 guidelines):</b>"))
ind_list = [
    "Symptomatic disease: bone pain, headache, neurological complications, pagetic radiculopathy/arthropathy",
    "Risk of future complications: weight-bearing bones, vertebral bodies, skull, bone adjacent to major joints",
    "Pre-operative: reduce vascularity and operative blood loss at pagetic surgical sites",
    "Immobilization hypercalciuria (prevent nephrolithiasis)",
    "High disease activity: ALP &gt;4x normal with involvement of high-risk sites",
    "Asymptomatic patients at risk: active disease near skull base, spine, major joints",
]
for i in ind_list:
    story.append(b(i))
story.append(sp(6))

story.append(h2("Pharmacological Management (MAINSTAY)"))
story.append(p(
    "<b>Bisphosphonates are the first-line treatment.</b> They suppress osteoclastic bone resorption, "
    "secondarily reducing osteoblastic formation. ALP normalises, and disordered pagetic bone is replaced "
    "by more organised lamellar bone."
))
drug_data = [
    ["Drug", "Route / Dose", "ALP Normalisation", "Notes / Exam Points"],
    ["Zoledronic acid\n(Zoledronate)", "5 mg IV single infusion\nover 15 min",
     "<b>~90%</b> at 6 months\n(BEST efficacy)",
     "<b>FIRST CHOICE</b> (esp. severe disease, rapid normalisation needed). "
     "Remission: 6.5 yrs avg. Flu-like side effects 1-3 days post-infusion."],
    ["Pamidronate", "30 mg/d IV x 3 days\n(4 hr infusion each)",
     "~50%", "IV bisphosphonate; used when oral not tolerated. "
     "Flu-like reaction; hypocalcemia risk."],
    ["Risedronate", "30 mg/d PO x 2 months", "~73%",
     "Oral; good efficacy. Must take on empty stomach, 30 min before food. "
     "2nd choice to zoledronate."],
    ["Alendronate", "40 mg/d PO x 6 months", "~63%",
     "Oral; requires strict dosing instructions to avoid esophageal irritation."],
    ["Tiludronate", "800 mg/d PO x 3 months", "~35%",
     "Less potent; less commonly used now."],
    ["Etidronate", "200-400 mg/d PO x 6 months", "~15%",
     "First-generation; least effective; impairs mineralisation if prolonged. "
     "Historical use only."],
    ["Calcitonin\n(Salmon calcitonin)", "100 U SC/IM daily x 6-18 mo\n(reduce to 50U 3x/wk)",
     "ALP falls ~50% (not normalised)",
     "SECOND LINE / adjunct. For patients intolerant of bisphosphonates. "
     "Analgesic effect. Nasal spray: less effective. Slower onset. "
     "Use before surgery if bisphosphonates not possible."],
]
story.append(simple_table(
    drug_data[0], drug_data[1:],
    col_widths=[3*cm, 3.5*cm, 3*cm, 5.5*cm]
))
story.append(sp(4))
story.append(exam_box(
    "ZOLEDRONIC ACID = drug of choice for Paget's (5 mg IV single dose, 90% ALP normalisation). "
    "Risedronate = best oral option. Etidronate = first gen, least effective, impairs mineralisation."
))

story.append(sp(6))
story.append(h3("Pre-Treatment Precautions"))
pre_list = [
    "Ensure adequate <b>calcium (1000-1500 mg/day)</b> and <b>Vitamin D (800-1000 IU/day)</b> supplementation "
    "BEFORE starting bisphosphonates (hypocalcemia risk, especially when bone formation continues after "
    "resorption is suppressed)",
    "Check renal function: <b>zoledronate is contraindicated</b> if eGFR &lt;35 mL/min/1.73m²",
    "Dental check before bisphosphonate therapy (osteonecrosis of jaw risk, though rare in Paget's doses)",
    "Monitor ALP every 3-6 months initially; then 6-12 monthly after remission",
]
for item in pre_list:
    story.append(b(item))

story.append(sp(6))
story.append(h3("Monitoring & Duration"))
mon_list = [
    "Target: <b>normalise ALP</b> (or reduce to within normal range) – indicates disease quiescence",
    "PINP and bone resorption markers (NTx, CTx) fall faster than ALP – useful for early treatment response",
    "Re-treat when ALP rises above normal or symptoms recur",
    "Zoledronate: remission typically lasts 2-7+ years; single infusion often sufficient for years",
    "No consensus on when to retreat; guided by ALP + symptoms + disease activity",
]
for m in mon_list:
    story.append(b(m))

# 1.7 ORTHOPAEDIC SURGICAL MANAGEMENT
story.append(sp(8))
story.append(h1("1.7  Orthopaedic Surgical Management"))
story.append(hr())
story.append(p(
    "<b>Medical treatment should PRECEDE surgery</b> whenever possible to reduce bone vascularity, "
    "decrease intraoperative blood loss, and facilitate healing."
))
surg_data = [
    ["Indication", "Procedure / Approach", "Key Points"],
    ["Fractures", "ORIF (nailing preferred for shaft fractures)\nCorrection osteotomy for deformity",
     "Intramedullary nail for femoral/tibial shaft fractures; avoid unlocking until bone disease suppressed. "
     "\"Chalk stick\" fractures tend to be transverse – easier to fix."],
    ["Secondary osteoarthritis\n(hip / knee)", "Total Hip Arthroplasty (THA) / Total Knee Arthroplasty (TKA)",
     "Most common indication. Pre-op bisphosphonates for 2-3 months. "
     "Anticipate altered bone anatomy, softer bone, increased bleeding. "
     "Protrusio acetabuli makes THA technically demanding."],
    ["Spinal stenosis / cord compression",
     "Laminectomy / decompression\nFusion for instability",
     "Medical management first (may resolve neurological symptoms). "
     "Surgery for progressive deficits, structural instability, or failure of medical therapy."],
    ["Osteotomy", "Corrective osteotomy of bowed tibia/femur",
     "Rarely needed; for severe deformity causing pain or gait problems. "
     "Pre-op medical treatment essential."],
    ["Sarcomatous transformation",
     "Wide surgical excision ± amputation\n+ Chemotherapy (sarcoma protocols)",
     "Extremely poor prognosis; managed as high-grade sarcoma. "
     "Radiation-resistant. Aggressive multi-modality treatment."],
]
story.append(simple_table(
    surg_data[0], surg_data[1:],
    col_widths=[3.5*cm, 4.5*cm, 7*cm]
))

# 1.8 PROGNOSIS
story.append(sp(8))
story.append(h1("1.8  Prognosis & Follow-up"))
story.append(hr())
prog_items = [
    "Overall prognosis is <b>good</b> with modern bisphosphonate therapy",
    "Mortality in uncomplicated Paget's = same as general population",
    "Sarcomatous transformation: survival &lt;1 year in majority; 5-yr survival &lt;5-10%",
    "Hearing loss is often irreversible even after treatment",
    "Follow-up: ALP every 3-6 months initially; imaging if new symptoms or rising ALP",
    "Secondary OA progression may be slowed but not reversed by bisphosphonates",
    "Newer data: prevalence and severity declining globally – possibly due to earlier treatment or "
    "decreased paramyxoviral exposure",
]
for pr in prog_items:
    story.append(b(pr))

# 1.9 HIGH-YIELD SUMMARY TABLE
story.append(sp(8))
story.append(h1("1.9  High-Yield Summary Table: Paget's Disease"))
story.append(hr())
hy_paget = [
    ["Feature", "Key Point"],
    ["Definition", "Localised bone remodelling disorder – osteoclast overactivity → disorganised bone"],
    ["Prevalence", "3% autopsy over 40; M > F; Western European origin"],
    ["Gene", "RANK (TNFRSF11A), OPG (TNFRSF11B), VCP mutations in familial forms"],
    ["Most common site", "Pelvis (most common single site), followed by spine, skull, femur, tibia"],
    ["Most common symptom", "Bone pain (+ warmth, deformity)"],
    ["Skull X-ray", "\"Cotton wool\" (mixed), osteoporosis circumscripta (lytic), enlargement"],
    ["Vertebra X-ray", "\"Picture frame\" vertebra; \"ivory vertebra\" (diffuse sclerosis)"],
    ["Long bone X-ray", "\"Blade of grass\" lytic advancing front; cortical thickening; bowing; "
     "\"chalk stick\" fractures"],
    ["Pathognomonic histology", "Mosaic/jigsaw cement lines (woven + lamellar bone)"],
    ["Best single biochemical marker", "Serum Total ALP (elevated); Ca & PO4 NORMAL"],
    ["Best imaging for extent", "Bone scan (Tc-99m HDP) – most sensitive"],
    ["1st-line treatment", "Zoledronic acid 5 mg IV (single dose) – 90% ALP normalisation"],
    ["Best oral drug", "Risedronate 30 mg/d x 2 months"],
    ["Dreaded complication", "Sarcomatous transformation (&lt;0.5%); osteosarcoma most common"],
    ["Most common ortho complication", "Secondary OA (hip > knee) requiring arthroplasty"],
    ["Surgery prep", "Bisphosphonates 2-3 months pre-op to reduce vascularity"],
    ["Fracture pattern", "Transverse \"chalk-stick\" in convexity of bowed bone"],
]
story.append(simple_table(hy_paget[0], hy_paget[1:], col_widths=[5*cm, 10*cm]))
story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# PART 2  ──  EWING'S SARCOMA
# ═══════════════════════════════════════════════════════════════════════════════
story.append(chapter_banner("PART 2: EWING'S SARCOMA", TEAL))
story.append(sp(10))

# 2.1 EPIDEMIOLOGY & MOLECULAR
story.append(h1("2.1  Epidemiology & Molecular Biology"))
story.append(hr())
story.append(p(
    "Ewing sarcoma is the <b>3rd most common primary malignant bone tumour</b> overall, "
    "but the <b>2nd most common after osteosarcoma in patients &lt;30 years</b>, and the "
    "<b>MOST COMMON in patients &lt;10 years</b>. It belongs to the Ewing family of tumours (EFT) which "
    "also includes peripheral primitive neuroectodermal tumour (PNET) and Askin tumour (chest wall PNET)."
))
epi2_data = [
    ["Parameter", "Detail"],
    ["Incidence", "&lt;1 per million per year; 2nd most common bone sarcoma in children/adolescents"],
    ["Age", "Peak: 10-20 years (5-25 years most cases); rare &gt;30 or &lt;5 years"],
    ["Sex", "Slightly higher in males (M:F ~1.5:1)"],
    ["Race", "Exceedingly rare in individuals of African descent; predominantly Caucasian"],
    ["Predisposing factors", "None known"],
    ["Common locations", "Diaphysis/metaphysis of long bones (femur most common); flat bones "
     "(pelvis, scapula, ribs); skull rare; small bones rare"],
    ["Chromosomal translocation",
     "<b>t(11;22)(q24;q12)</b> – present in >85% of cases → EWS-FLI1 fusion gene "
     "(EWS gene on chr 22, FLI1 on chr 11). Other: t(21;22) → EWS-ERG; t(7;22) → EWS-ETV1"],
    ["Molecular marker", "CD99 (MIC-2 gene product) – strong membranous positivity; "
     "highly sensitive (not 100% specific)"],
]
story.append(simple_table(epi2_data[0], epi2_data[1:], col_widths=[4*cm, 11*cm]))
story.append(sp(6))
story.append(exam_box(
    "t(11;22)(q24;q12) = pathognomonic translocation (EWS-FLI1 fusion). "
    "CD99 (MIC-2) positivity = key IHC marker. "
    "Most common in ages 10-20; MOST common bone tumour under 10 years."
))

# 2.2 PATHOLOGY
story.append(sp(8))
story.append(h1("2.2  Pathology & Classification"))
story.append(hr())
story.append(p("<b>Gross:</b> Soft, grey-white tumour with areas of necrosis and haemorrhage, often with large "
    "soft tissue mass extending through cortex."))
story.append(p(
    "<b>Histology:</b> Small round blue cell tumour – uniform, small cells with scant cytoplasm, "
    "round nuclei with fine chromatin, arranged in sheets with little intercellular matrix. "
    "Homer-Wright pseudorosettes may be seen (PNET variant). "
    "PAS stain: <b>glycogen-positive</b> (intracellular glycogen)."
))
story.append(p("<b>Immunohistochemistry (IHC) panel:</b>"))
ihc_data = [
    ["Marker", "Ewing Sarcoma", "Utility"],
    ["CD99 (MIC-2)", "<b>Strongly +ve</b> (membranous)", "Most useful; high sensitivity"],
    ["FLI-1", "+ ve (nuclear)", "Correlates with EWS-FLI1 fusion"],
    ["Vimentin", "+ ve", "Non-specific"],
    ["NSE (neuron-specific enolase)", "+/- (PNET)", "Neural differentiation"],
    ["S100", "Focal + (PNET)", ""],
    ["Cytokeratin", "NEGATIVE", "Important differential: rules out carcinoma"],
    ["LCA (CD45)", "NEGATIVE", "Rules out lymphoma"],
    ["Desmin", "NEGATIVE", "Rules out rhabdomyosarcoma"],
    ["CD99 note", "Also + in lymphoblastic lymphoma", "Correlation with clinical + molecular needed"],
]
story.append(simple_table(ihc_data[0], ihc_data[1:], col_widths=[5*cm, 4*cm, 6*cm]))
story.append(sp(4))
story.append(p(
    "<b>Differential diagnosis of small round blue cell tumours (mnemonic: LEMON):</b>"
))
story.append(b("<b>L</b>ymphoma / lymphoblastic lymphoma"))
story.append(b("<b>E</b>wing sarcoma / PNET"))
story.append(b("<b>M</b>esenchymal chondrosarcoma"))
story.append(b("<b>O</b>steosarcoma (small cell variant)"))
story.append(b("<b>N</b>euroblastoma / rhabdomyosarcoma"))
story.append(exam_box(
    "Small round blue cell tumour + CD99 positive + t(11;22) = Ewing's sarcoma. "
    "PAS positive (glycogen). NEGATIVE for cytokeratin (not carcinoma) and LCA (not lymphoma)."
))

# 2.3 CLINICAL FEATURES
story.append(sp(8))
story.append(h1("2.3  Clinical Features"))
story.append(hr())
story.append(p(
    "<b>Pain is almost universal</b> and the most common presenting complaint. "
    "Onset is insidious; may respond initially to conservative treatment, leading to diagnostic delay. "
    "<b>Average delay from symptom onset to diagnosis: 34 weeks</b> "
    "(patient delay ~15 weeks, physician delay ~19 weeks)."
))
story.append(sp(4))
cf2_data = [
    ["Feature", "Details"],
    ["Pain", "Dull, aching; may be intermittent initially; worsens as tumour grows"],
    ["Swelling / soft tissue mass", "Often present; may be large and palpable; warmth and tenderness"],
    ["Fever / constitutional symptoms",
     "Can mimic osteomyelitis – fever, elevated WBC, elevated ESR/CRP. "
     "CLASSIC MIMIC – most important differential diagnosis in children."],
    ["Pathological fracture", "10-20% present with pathological fracture"],
    ["Neurological symptoms", "If spine/pelvis involved: weakness, paraplegia, bowel/bladder dysfunction"],
    ["Metastatic symptoms",
     "Lung metastases: dyspnoea, chest pain; bone metastases: diffuse pain; "
     "bone marrow: anaemia, thrombocytopenia"],
]
story.append(simple_table(cf2_data[0], cf2_data[1:], col_widths=[4*cm, 11*cm]))
story.append(sp(4))
story.append(warning_box(
    "Ewing sarcoma mimics osteomyelitis. Pus-like material on needle aspiration may be sent to "
    "microbiology only – ALWAYS send biopsy specimens to BOTH culture AND pathology."
))
story.append(sp(4))
story.append(p("<b>Laboratory findings:</b>"))
lab_list = [
    "Elevated ESR, CRP, WBC (mimics infection)",
    "<b>Elevated LDH (lactate dehydrogenase)</b> – poor prognostic marker",
    "Anaemia of chronic disease",
    "Normal or elevated alkaline phosphatase",
]
for ll in lab_list:
    story.append(b(ll))

# 2.4 IMAGING
story.append(sp(8))
story.append(h1("2.4  Imaging & Staging Workup"))
story.append(hr())
img_data = [
    ["Modality", "Findings / Role"],
    ["Plain X-ray (first-line)",
     "<b>\"Onion skin\" periosteal reaction</b> (laminated periostitis) – classic but variable. "
     "Diaphyseal destructive lesion; \"moth-eaten\" permeative pattern (Lodwick III). "
     "\"Sunburst\" periosteal reaction (less common than osteosarcoma). "
     "Codman's triangle may be present. Large soft tissue component often underestimated on X-ray."],
    ["MRI (MANDATORY for all cases)",
     "Mandatory for full extent of lesion and soft tissue mass. "
     "Entire bone must be imaged (lesion typically extends beyond X-ray abnormality). "
     "T1 hypointense, T2 hyperintense with large soft tissue component. "
     "Used to plan surgery/radiation field. Repeat after neoadjuvant chemotherapy."],
    ["CT Chest",
     "Baseline staging – lungs are MOST COMMON site of metastasis (&gt;20% at presentation). "
     "Repeat after chemotherapy before definitive local treatment."],
    ["Bone Scan (Tc-99m)",
     "Bone is 2nd most common site of metastasis. Detects skip lesions and distant bone involvement."],
    ["FDG PET/CT",
     "High sensitivity and accuracy for diagnosis, staging, detection of recurrence. "
     "Can replace bone marrow biopsy at some centres for staging."],
    ["Bone marrow biopsy",
     "Posterior iliac crest biopsy at some centres to rule out diffuse marrow involvement. "
     "FDG-PET increasingly used as alternative."],
    ["Whole-body MRI",
     "Some centres use for staging; excellent sensitivity for bone marrow involvement."],
]
story.append(simple_table(img_data[0], img_data[1:], col_widths=[4*cm, 11*cm]))
story.append(sp(4))
story.append(exam_box(
    "Classic X-ray = \"onion skin\" periosteal reaction in diaphysis. "
    "MRI of entire bone is mandatory. Lungs = most common metastatic site. "
    "Always check chest CT and bone scan at baseline."
))

# 2.5 STAGING
story.append(sp(8))
story.append(h1("2.5  Staging (Enneking System for Bone Sarcomas)"))
story.append(hr())
story.append(p(
    "All Ewing sarcomas are <b>high grade (Grade II)</b> by definition. "
    "Enneking staging is used for surgical planning."
))
enneking_data = [
    ["Stage", "Grade", "Site", "Metastasis", "Ewing's Sarcoma"],
    ["IA", "G1 (Low)", "T1 (Intracompartmental)", "M0", "Not applicable (Ewing = always high grade)"],
    ["IB", "G1 (Low)", "T2 (Extracompartmental)", "M0", "Not applicable"],
    ["IIA", "G2 (High)", "T1 (Intracompartmental)", "M0", "Localised, intracompartmental"],
    ["IIB", "G2 (High)", "T2 (Extracompartmental)", "M0", "<b>MOST Ewing's sarcomas at diagnosis</b>"],
    ["III", "Any", "Any", "M1 (Regional/distant)", "~25% at presentation (poor prognosis)"],
]
story.append(simple_table(
    enneking_data[0], enneking_data[1:],
    col_widths=[2*cm, 2.5*cm, 4*cm, 2.5*cm, 4*cm]
))
story.append(sp(4))
story.append(p(
    "<b>Resection margins (Enneking):</b> Intralesional → Marginal → Wide → Radical. "
    "Goal in Ewing sarcoma: <b>WIDE excision</b> margins when surgery is chosen for local control."
))

# 2.6 MANAGEMENT ─── EMPHASIS
story.append(sp(8))
story.append(h1("2.6  Management of Ewing's Sarcoma"))
story.append(hr(color=TEAL, width=2))
story.append(p(
    "Management is <b>multimodal</b> and requires a multidisciplinary team (orthopaedic oncologist, "
    "medical oncologist, radiation oncologist, paediatric oncologist, pathologist, radiologist). "
    "<b>CHEMOTHERAPY IS MANDATORY</b> – without it, long-term survival was &lt;10%; "
    "with modern multidrug regimens, 60-75% 5-year survival for localised disease."
))
story.append(sp(4))

story.append(h2("Overview of Treatment Strategy"))
overview_data = [
    ["Phase", "Treatment", "Duration / Details"],
    ["1. Neoadjuvant Chemotherapy", "VDC/IE alternating regimen", "~12-18 weeks (4-6 cycles)"],
    ["2. Local Control", "Surgery (Wide resection) ± Radiation", "After neoadjuvant chemo; re-stage first"],
    ["3. Adjuvant Chemotherapy", "Continue same regimen", "Total ~10-12 months"],
    ["Metastatic disease", "Same ± high-dose chemo + auto-SCT", "Poor prognosis; investigational"],
]
story.append(simple_table(
    overview_data[0], overview_data[1:],
    col_widths=[4.5*cm, 5.5*cm, 5*cm]
))
story.append(sp(6))

story.append(h2("A. Chemotherapy (Systemic Treatment)"))
story.append(p(
    "Chemotherapy treats <b>micrometastatic disease</b> that is assumed present at diagnosis "
    "(hence the pre-surgical 'neoadjuvant' phase) and reduces local tumour volume to facilitate surgery."
))
story.append(p("<b>Standard Regimen – VDC/IE alternating (every 2-3 weeks):</b>"))

chemo_data = [
    ["Regimen", "Drugs", "Cycle"],
    ["VDC", "Vincristine + Doxorubicin (Adriamycin) + Cyclophosphamide",
     "Alternates with IE (every 2 weeks with G-CSF support)"],
    ["IE", "Ifosfamide + Etoposide", "Alternates with VDC"],
    ["VAIA (European)", "Vincristine + Actinomycin-D + Ifosfamide + Doxorubicin",
     "Used in European EURO-EWING studies"],
    ["VIDE (induction)", "Vincristine + Ifosfamide + Doxorubicin + Etoposide",
     "Induction in Euro-Ewing 99; 6 cycles"],
    ["High-dose chemo + auto-SCT",
     "Busulfan + Melphalan (BuMel)", "For high-risk/metastatic: consolidation after standard chemo"],
]
story.append(simple_table(
    chemo_data[0], chemo_data[1:],
    col_widths=[3.5*cm, 6*cm, 5.5*cm]
))
story.append(sp(4))
story.append(p("<b>Response assessment:</b> After neoadjuvant chemotherapy:"))
resp_list = [
    "Repeat staging: X-ray (increased ossification), repeat MRI (decreased soft tissue mass), CT chest",
    "<b>Histological response:</b> &gt;95-100% necrosis = \"good response\" (favourable prognosis)",
    "Good histological response associated with improved event-free survival",
    "FDG-PET has prognostic value for assessing histological response",
]
for r in resp_list:
    story.append(b(r))
story.append(sp(6))

story.append(h2("B. Local Control: Surgery vs Radiation"))
story.append(p(
    "<b>Ewing sarcoma is radiosensitive</b> (unlike osteosarcoma), so radiation is a viable option. "
    "However, wide surgical resection is preferred when feasible due to lower local recurrence rates "
    "and avoidance of radiation-related complications (growth disturbance, secondary malignancy, fibrosis)."
))
story.append(sp(4))

lc_data = [
    ["Modality", "Indications", "Advantages", "Disadvantages"],
    ["Wide Surgical\nResection\n(PREFERRED)",
     "Accessible lesion; acceptable functional deficit; wide margins achievable; "
     "distal extremity tumours; small tumours after chemo response",
     "Lower local recurrence (&lt;10%); avoids radiation sequelae; "
     "allows histological response grading; better local control",
     "Functional loss; complex reconstruction needed; "
     "not feasible for all locations"],
    ["Radiation Therapy\n(ALTERNATIVE)",
     "Unresectable tumour (spine, pelvis, skull); would require mutilating surgery; "
     "marginal/contaminated resection; patient refusal of surgery",
     "Limb/organ preservation; avoids surgery morbidity; "
     "effective for radiosensitive Ewing's",
     "Long-term: growth disturbance in children, secondary sarcoma risk (1-2%), "
     "fibrosis, functional loss; does not allow histological grading"],
    ["Surgery + Radiation\n(COMBINED)",
     "Marginal or contaminated wide resection; close/positive margins post-resection",
     "Better local control than either alone when margins involved",
     "Cumulative side effects of both modalities"],
]
story.append(simple_table(
    lc_data[0], lc_data[1:],
    col_widths=[3*cm, 4.5*cm, 4*cm, 3.5*cm]
))
story.append(sp(4))
story.append(exam_box(
    "Surgery = preferred local treatment (lower local recurrence). "
    "Radiation = equally effective but has late complications (secondary sarcoma, growth arrest). "
    "Choice is INDIVIDUALISED. ALWAYS precede local treatment with neoadjuvant chemo."
))

story.append(sp(6))
story.append(h2("C. Limb Salvage vs. Amputation"))
limb_data = [
    ["Factor", "Favour Limb Salvage", "Favour Amputation"],
    ["Tumour response to chemo", "Good response (&gt;90-95% necrosis)", "Poor response; persistent large tumour"],
    ["Surgical margins achievable", "Wide margins achievable", "Only marginal or intralesional possible"],
    ["Vascular/nerve involvement", "Not encasing major vessels/nerves", "Encasing major neurovascular structures"],
    ["Functional outcome", "Expected function &gt; amputation", "Pathological fracture with contamination"],
    ["Patient age", "Older child/adult", "Very young child (complex reconstruction)"],
    ["Infection / complication", "No prior infection", "Fungal/refractory infection post-radiation"],
]
story.append(simple_table(
    limb_data[0], limb_data[1:],
    col_widths=[4*cm, 5.5*cm, 5.5*cm]
))
story.append(sp(4))
story.append(p(
    "<b>Surgical reconstruction options:</b> Endoprosthesis (most common); osteoarticular allograft; "
    "allograft-prosthesis composite; vascularised fibular graft; arthrodesis; rotationplasty "
    "(Winkelmann procedure for distal femur in young children)."
))

story.append(sp(6))
story.append(h2("D. Pelvic Ewing's Sarcoma – Special Considerations"))
story.append(p(
    "Pelvic Ewing's sarcoma carries the worst prognosis among all sites. "
    "Local treatment is controversial:"
))
pelv_list = [
    "Some centres treat with definitive radiation therapy alone (given morbidity of pelvic surgery)",
    "Others advocate combined surgery + radiation for better local control",
    "Large tumour size at presentation is common (poor prognosis)",
    "Internal hemipelvectomy or hindquarter amputation for unresectable disease",
    "Sacral involvement: complex reconstruction; high recurrence risk",
]
for pp in pelv_list:
    story.append(b(pp))

# 2.7 RADIATION
story.append(sp(8))
story.append(h1("2.7  Radiation Therapy Details"))
story.append(hr())
rad_data = [
    ["Aspect", "Details"],
    ["Radiosensitivity", "Ewing sarcoma is HIGHLY radiosensitive (unlike osteosarcoma)"],
    ["Definitive dose", "45-55.8 Gy in 1.8 Gy fractions to involved field (IMRT preferred)"],
    ["Adjuvant dose (post-surgery)", "45-50.4 Gy for positive/close margins"],
    ["Field", "Pre-chemo tumour volume + margin (not whole bone, reducing growth complications)"],
    ["Timing", "Concurrent with final chemotherapy cycles or after surgery"],
    ["Metastatic lung disease", "Whole lung irradiation (WLI): 15-18 Gy; consider for pulmonary metastases"],
    ["Complications", "Acute: skin reaction, fatigue, nausea. Late: secondary sarcoma (1-2%), "
     "growth plate arrest in children, joint fibrosis, lymphedema, bowel complications (pelvic RT)"],
    ["Children – special", "Growth disturbance; consider proton beam therapy to reduce dose to "
     "normal tissues if available"],
]
story.append(simple_table(rad_data[0], rad_data[1:], col_widths=[4.5*cm, 10.5*cm]))

# 2.8 PROGNOSIS
story.append(sp(8))
story.append(h1("2.8  Prognosis & Prognostic Factors"))
story.append(hr())
story.append(p(
    "Overall 5-year survival: <b>60-75% for localised disease</b> with current multimodal treatment. "
    "Prior to chemotherapy: &lt;10%. <b>Metastatic disease at presentation: 20-30%</b> long-term survival."
))
story.append(sp(4))

pf_data = [
    ["Prognostic Factor", "Good Prognosis", "Poor Prognosis"],
    ["Metastases at presentation", "Absent (localised disease)", "<b>WORST factor:</b> metastases present"],
    ["Histological response to chemo", "&gt;95% necrosis (good response)", "&lt;90% necrosis (poor response)"],
    ["Tumour size / volume", "&lt;8 cm / &lt;200 mL", "&gt;8 cm / &gt;200 mL (most important size cutoff)"],
    ["Location", "Distal extremity", "Pelvis, proximal femur, spine, ribs"],
    ["LDH level", "Normal", "<b>Elevated LDH = poor prognosis</b>"],
    ["Age", "Younger (&lt;14-15 years)", "Older age (&gt;17-18 years)"],
    ["Fever / WBC / ESR", "Absent", "Present (indicates more extensive disease)"],
    ["Time to relapse", "Late (&gt;2 years)", "Early relapse (&lt;2 years) – very poor outcome"],
    ["Histological grade", "N/A", "All Ewing = high grade (no prognostic value within Ewing)"],
    ["p53 mutation", "Absent", "p53 mutation = poor prognostic marker"],
]
story.append(simple_table(
    pf_data[0], pf_data[1:],
    col_widths=[4.5*cm, 4*cm, 6.5*cm]
))
story.append(sp(4))
story.append(exam_box(
    "WORST prognostic factor = metastases at presentation (only 20-30% survival). "
    "Histological response (&gt;95% necrosis) is key. Elevated LDH = poor prognosis. "
    "Pelvis = worst site; distal extremity = best site."
))

# 2.9 RECURRENCE
story.append(sp(8))
story.append(h1("2.9  Recurrence & Salvage Treatment"))
story.append(hr())
rec_list = [
    "Overall recurrence rate: ~30-40% for localised disease; higher for metastatic disease",
    "<b>Local recurrence:</b> ~20% 5-year survival",
    "<b>Distant recurrence (metastases):</b> ~10% 5-year survival",
    "Time to relapse is critical: relapse within 1 year = &lt;10% survival; "
    "late relapse (&gt;2 years) = marginally better outcomes",
    "<b>Salvage options:</b> Second-line chemotherapy (topotecan + cyclophosphamide; "
    "irinotecan + temozolomide); re-irradiation; surgery; high-dose chemo + autologous SCT",
    "Novel agents under investigation: IGF-1R inhibitors, PARP inhibitors, immune checkpoint inhibitors",
    "Follow-up imaging: chest X-ray/CT 3-6 monthly for first 2-3 years; then annually",
]
for rl in rec_list:
    story.append(b(rl))

# 2.10 HIGH-YIELD SUMMARY
story.append(sp(8))
story.append(h1("2.10  High-Yield Summary Table: Ewing's Sarcoma"))
story.append(hr())
hy_ewing = [
    ["Feature", "Key Point"],
    ["Family", "Ewing family of tumours: Ewing sarcoma, PNET, Askin tumour (chest wall)"],
    ["Age", "Peak 10-20 years; most common bone sarcoma &lt;10 years"],
    ["Race", "Rare in individuals of African descent"],
    ["Translocation", "<b>t(11;22)(q24;q12)</b> → EWS-FLI1 fusion (&gt;85%)"],
    ["IHC", "CD99 (MIC-2) strongly +ve; PAS +ve (glycogen); CK negative; LCA negative"],
    ["Histology", "Small round blue cells; sheet-like; Homer-Wright pseudorosettes in PNET"],
    ["Most common site", "Femoral diaphysis / metaphysis; flat bones (pelvis, ribs)"],
    ["Classic X-ray", "\"Onion skin\" periosteal reaction; diaphyseal permeative destruction"],
    ["Soft tissue mass", "Typically large; better seen on MRI"],
    ["Most common metastatic site", "Lungs (CT chest mandatory)"],
    ["Mimic", "Osteomyelitis (fever, elevated WBC, ESR, pus-like aspirate)"],
    ["Differential (small blue cells)", "Lymphoma, PNET, neuroblastoma, rhabdomyosarcoma, "
     "small cell osteosarcoma"],
    ["Staging system", "Enneking; all Ewing = high grade (Grade II/III)"],
    ["Treatment principle", "Multimodal: CHEMO (mandatory) + Local control (surgery preferred)"],
    ["Standard chemo", "VDC/IE alternating (Vincristine + Doxorubicin + Cyclophosphamide / "
     "Ifosfamide + Etoposide)"],
    ["Local treatment preferred", "Wide surgical resection (if feasible, &lt;10% local recurrence)"],
    ["Radiation", "Alternative to surgery; effective but risk of secondary sarcoma"],
    ["Good histological response", "&gt;95% necrosis after neoadjuvant chemo = favourable prognosis"],
    ["5-year survival (localised)", "60-75% with modern treatment"],
    ["5-year survival (metastatic)", "20-30%"],
    ["Worst prognostic factor", "Metastases at presentation"],
    ["LDH significance", "Elevated LDH = poor prognosis"],
    ["Local recurrence survival", "~20% at 5 years"],
    ["Distant recurrence survival", "~10% at 5 years"],
]
story.append(simple_table(hy_ewing[0], hy_ewing[1:], col_widths=[5*cm, 10*cm]))
story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════════════════════
# PART 3  ──  COMPARISON TABLE
# ═══════════════════════════════════════════════════════════════════════════════
story.append(chapter_banner("PART 3: PAGET'S DISEASE vs. EWING'S SARCOMA – COMPARISON", MID_BLUE))
story.append(sp(10))

comp_tbl_data = [
    ["Parameter", "Paget's Disease of Bone", "Ewing's Sarcoma"],
    ["Definition", "Metabolic bone disease; disordered remodelling",
     "Primary malignant bone tumour; Ewing family of tumours"],
    ["Age", "Rare &lt;40; mostly 50-70+ years", "Peak 10-20 years; most common &lt;10 years"],
    ["Sex", "Males &gt; Females", "Slightly more in males"],
    ["Aetiology/Genetics",
     "RANK/OPG mutations; paramyxoviral (?); multifactorial",
     "t(11;22)(q24;q12) → EWS-FLI1 fusion"],
    ["Common sites", "Pelvis, spine, skull, femur, tibia",
     "Femoral diaphysis, pelvis, ribs; any site"],
    ["Pain", "Aching, chronic, site-specific", "Insidious, may mimic osteomyelitis"],
    ["Classic X-ray", "\"Cotton wool\" skull; \"Picture frame\" vertebra; "
     "\"Blade of grass\" long bone",
     "\"Onion skin\" periosteal reaction; permeative destruction"],
    ["Biochemical markers", "Elevated ALP; normal Ca & PO4",
     "Elevated ESR, WBC, LDH; ALP variable"],
    ["Histology", "Mosaic cement lines; giant osteoclasts; "
     "disorganised woven + lamellar bone",
     "Small round blue cells; PAS+ve; CD99 strongly +ve"],
    ["Diagnosis", "X-ray + Bone scan + ALP", "X-ray + MRI + Biopsy + Molecular"],
    ["Mainstay treatment", "Bisphosphonates (zoledronic acid first-line)",
     "Multimodal: Chemo (VDC/IE) + Surgery ± Radiation"],
    ["Role of surgery", "THA/TKA for OA; ORIF for fractures; "
     "decompression for neural compression",
     "Wide resection = preferred local control; amputation if needed"],
    ["Radiation therapy", "Used for bone pain (palliative); "
     "giant cell tumour variant responds to steroids",
     "Definitive or adjuvant; tumour is radiosensitive"],
    ["Chemotherapy", "Not primary treatment; sarcoma chemo if "
     "transformation occurs",
     "MANDATORY; VDC/IE; neoadjuvant + adjuvant"],
    ["Dreaded complication", "Sarcomatous transformation (&lt;0.5%): osteosarcoma",
     "Metastases (lungs, bone); recurrence"],
    ["Prognosis (overall)", "Good with treatment; normal life expectancy except if sarcoma",
     "Localised: 60-75% 5-yr survival; Metastatic: 20-30%"],
    ["Worst outcome scenario", "Sarcomatous transformation (&lt;5-10% 5-yr survival)",
     "Metastases at presentation (20-30% 5-yr survival)"],
    ["Monitoring", "ALP (3-6 monthly); bone scan for extent",
     "CT chest + bone scan/PET every 3-6 months x 3 years"],
]
story.append(simple_table(
    comp_tbl_data[0], comp_tbl_data[1:],
    col_widths=[4*cm, 6.5*cm, 4.5*cm]
))

story.append(sp(12))
story.append(h1("Quick Recall Mnemonics"))
story.append(hr())

story.append(h2("Paget's Disease of Bone – \"PAGETS\""))
mnem_paget = [
    "<b>P</b> – Pelvis (most common site); Pain (dull, aching)",
    "<b>A</b> – ALP elevated (normal Ca; normal PO4)",
    "<b>G</b> – Giant osteoclasts; Genetics (RANK, OPG mutations)",
    "<b>E</b> – Enlarged skull / bone; Etidronate (first gen, least effective)",
    "<b>T</b> – Transformation to sarcoma (&lt;0.5%); Tibia (sabre tibia, chalk-stick Fx)",
    "<b>S</b> – Scan (bone scan = most sensitive); Surgery pre-treat with bisphosphonates; "
    "Zoledronic acid = 1st choice (5 mg IV = 90% ALP normalisation)",
]
for m in mnem_paget:
    story.append(b(m))

story.append(sp(6))
story.append(h2("Ewing's Sarcoma – \"EWING\""))
mnem_ewing = [
    "<b>E</b> – EWS-FLI1 fusion t(11;22); Elevated LDH = poor prognosis",
    "<b>W</b> – White cell count elevated (mimics osteomyelitis); Wide resection preferred",
    "<b>I</b> – IE regimen (Ifosfamide + Etoposide) alternates with VDC; "
    "IHC = CD99 strongly positive",
    "<b>N</b> – No African descent affected (exceedingly rare); Neoadjuvant chemo MANDATORY",
    "<b>G</b> – Glycogen positive (PAS+ve); Grade II/III (all high grade); "
    "Good response = &gt;95% necrosis",
]
for m in mnem_ewing:
    story.append(b(m))

story.append(sp(12))
story.append(hr(color=DARK_BLUE))
story.append(Paragraph(
    "References: Campbell's Operative Orthopaedics 15th Ed 2026 | Harrison's 22E 2025 | "
    "Rockwood &amp; Green's 10th Ed 2025 | Miller's Review of Orthopaedics 9th Ed | "
    "Katzung Pharmacology 16th Ed | Firestein &amp; Kelley's Rheumatology | "
    "Endocrine Society Paget's Guidelines 2014",
    S_footer_note
))

# ─── PAGE NUMBER HEADER/FOOTER ────────────────────────────────────────────────
def on_page(canvas, doc):
    canvas.saveState()
    # Header
    canvas.setFillColor(DARK_BLUE)
    canvas.setFont("Helvetica-Bold", 8)
    canvas.drawString(2*cm, H - 1.5*cm, "Paget's Disease & Ewing's Sarcoma | Ortho PG Notes")
    canvas.drawRightString(W - 2*cm, H - 1.5*cm, "July 2026")
    # Footer
    canvas.setFont("Helvetica", 8)
    canvas.setFillColor(HexColor("#718096"))
    canvas.drawCentredString(W/2, 1.4*cm, f"Page {doc.page}")
    canvas.setStrokeColor(DARK_BLUE)
    canvas.setLineWidth(0.5)
    canvas.line(2*cm, H - 1.8*cm, W - 2*cm, H - 1.8*cm)
    canvas.line(2*cm, 1.8*cm, W - 2*cm, 1.8*cm)
    canvas.restoreState()

doc.build(story, onFirstPage=on_page, onLaterPages=on_page)
print(f"PDF generated: {OUTPUT_PATH}")
print(f"File size: {os.path.getsize(OUTPUT_PATH) / 1024:.1f} KB")