Actually tomorrow is my psm exam and i dont know about Assessment of nutritional status . Please provide me answer is easy language and easy way i will write in exam without forgeting

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Finding Sources

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I now have enough comprehensive content from Park's PSM textbook. Let me now give you a thorough, exam-ready answer.

Assessment of Nutritional Status

(Park's PSM - Easy Exam Notes)

What is it? (Write this as your intro)

Nutritional status = result of many interrelated factors (food intake quantity + quality + physical health of the individual).

Purpose of nutritional assessment:

Know prevalence and distribution of nutritional problems in a community
Identify individuals/groups "at risk"
Develop health care programmes
Evaluate effectiveness of nutrition programmes

7 Methods of Assessment (CABFVEE - Memory Trick)

Remember: "C A B F V E E"

#	Method	Memory Word
1	Clinical examination	C
2	Anthropometry	A
3	Biochemical evaluation	B
4	Functional assessment	F
5	Vital & health statistics	V
6	Ecological studies	E
7	Evaluation of dietary intake	E

These methods are not mutually exclusive - they are complementary to each other.

1. Clinical Examination

Simplest and most practical method
Examine for physical signs of malnutrition
Signs may be specific (e.g., Bitot's spots = Vit A deficiency) or non-specific
When 2 or more signs of same deficiency appear together = diagnostic significance increases

WHO classified signs into 3 groups:

Group 1 - Definite signs (strongly associated with malnutrition): e.g., Bitot's spots, corneal xerosis, follicular keratosis, angular stomatitis, scrotal/vulval dermatosis, oedema
Group 2 - Possible signs (may or may not be due to malnutrition): e.g., moist and swollen lips (cheilosis), thyroid enlargement, conjunctival pallor, dental caries
Group 3 - Non-specific signs (not reliably associated): e.g., nasal discharge, skin infections

2. Anthropometry

Measurement of body dimensions to assess nutritional status

Measurements used:

Weight and Height (most common)
MUAC - Mid-Upper Arm Circumference (very useful in children; no need for age)
Skinfold thickness - measures fat stores (Triceps, Subscapular, Suprailiac)
Head circumference - in children under 3 years
Chest circumference

Key Indices:

Index	What it measures	Used for
Weight for Age	Overall nutrition	Children; detects underweight
Height for Age	Chronic malnutrition	Detects stunting
Weight for Height	Acute malnutrition	Detects wasting
BMI = Wt(kg)/Ht²(m)	Obesity/thinness	Adults

BMI Cut-offs (Adults):

< 18.5 = Underweight (CED - Chronic Energy Deficiency)
18.5-24.9 = Normal
25-29.9 = Overweight
≥ 30 = Obese

MUAC Cut-offs:

< 12.5 cm = Severe malnutrition (children 1-5 yrs)
12.5-13.5 cm = Moderate malnutrition
13.5 cm = Normal

Grading of PEM (Protein Energy Malnutrition) - Gomez Classification (Weight for Age):

Grade I (Mild) = 75-90% of standard
Grade II (Moderate) = 60-74% of standard
Grade III (Severe) = < 60% of standard

3. Biochemical Evaluation

Detects subclinical deficiency before clinical signs appear = most sensitive method

Commonly used tests:

Nutrient	Test
Protein status	Serum albumin, transferrin, prealbumin
Iron deficiency	Haemoglobin, serum ferritin, serum iron
Vitamin A	Serum retinol
Iodine	Urinary iodine excretion
Vitamin D	Serum 25-OH Vitamin D
Vitamin C	Serum/urine ascorbic acid

Key Point: Biochemical tests detect subclinical (latent) deficiency - useful in early stages before symptoms appear.

4. Functional Assessment

Assesses functional consequences of malnutrition

Immune function - delayed hypersensitivity (skin test)
Muscle function - hand grip strength (dynamometer)
Work capacity / Physical performance
Cognitive function - mental development in children
Dark adaptation test - for Vitamin A deficiency

5. Assessment of Dietary Intake

Measures what food the person actually eats

Methods:

Method	Description
24-hour recall	Ask person to recall everything eaten in last 24 hours (most commonly used)
Dietary history	Long-term usual diet pattern (3-day, 7-day recall)
Food frequency questionnaire	How often certain foods are eaten
Weighed food record	Actual weighing of food before eating
Food balance sheets	National food availability data

Compare intake with RDA (Recommended Dietary Allowance) to identify deficiencies.

6. Vital and Health Statistics

Uses existing records - indirect method

IMR (Infant Mortality Rate) - sensitive indicator of nutritional status
Child mortality rate (especially 1-4 years = "toddler mortality")
Birth weight - indicator of maternal nutrition
Prevalence of low birth weight (< 2500 g)
Morbidity data - frequency of infections

7. Ecological Studies

Studies the environment and factors affecting nutrition

Food production data
Food consumption at national/community level
Socioeconomic factors (income, poverty, education)
Agricultural statistics
Food availability and distribution

Quick Summary Table (Write in Exam!)

Method	Detects	Best For
Clinical	Frank/obvious deficiency	Quick field surveys
Anthropometry	Growth retardation, obesity	Children mainly
Biochemical	Subclinical deficiency	Early detection
Functional	Consequences of malnutrition	Functional capacity
Dietary	Inadequate intake	Cause finding
Vital statistics	Population-level trends	Community surveys
Ecological	Risk factors	Planning programmes

Important Extra Points (Exam Favorites!)

Nutritional Surveillance = continuous monitoring of nutritional status of a population over time to detect changes and take action.

Indicators used for surveillance:

Birth weight (maternal nutrition)
Height for age, Weight for age (child nutrition)
Arm circumference (when age unknown)

Difference between nutritional assessment vs nutritional surveillance:

Assessment = done at one point in time (cross-sectional)
Surveillance = done continuously over time (monitoring)

Memory Tips for Exam

7 methods = C-A-B-F-D-V-E (Clinical, Anthropometry, Biochemical, Functional, Dietary, Vital stats, Ecological)
Most practical method = Clinical examination
Most sensitive method = Biochemical (detects subclinical)
Best for children when age unknown = MUAC or Weight for Height
Wasting = low weight for height (acute)
Stunting = low height for age (chronic)
Underweight = low weight for age (composite)

Good luck in your exam tomorrow! You've got this. Focus especially on the 7 methods, anthropometric indices (wasting/stunting/underweight), and BMI cut-offs - these are the most commonly asked topics in PSM exams.

Create a quick reference PDF summary for this topic

Reading File

Running Command

ls -la /home/daytona/workspace/ 2>/dev/null || echo "workspace empty or not found"

Running Command

mkdir -p /home/daytona/workspace/nutrition-psm && python3 -c "from reportlab.platypus import SimpleDocTemplate; print('reportlab ok')"

Writing File

~/nutrition-psm/generate_pdf.py

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

OUTPUT = "/home/daytona/workspace/nutrition-psm/Nutritional_Status_Assessment_PSM.pdf"

# ── Colour palette ────────────────────────────────────────────────
TEAL       = colors.HexColor("#0D7377")
TEAL_LIGHT = colors.HexColor("#E8F6F7")
ORANGE     = colors.HexColor("#E67E22")
ORANGE_LT  = colors.HexColor("#FEF9E7")
GREEN      = colors.HexColor("#1E8449")
GREEN_LT   = colors.HexColor("#EAFAF1")
PURPLE     = colors.HexColor("#6C3483")
PURPLE_LT  = colors.HexColor("#F5EEF8")
RED        = colors.HexColor("#C0392B")
GREY_DARK  = colors.HexColor("#2C3E50")
GREY_MID   = colors.HexColor("#7F8C8D")
GREY_LIGHT = colors.HexColor("#ECF0F1")
WHITE      = colors.white

# ── Styles ────────────────────────────────────────────────────────
styles = getSampleStyleSheet()

def S(name, **kw):
    return ParagraphStyle(name, **kw)

title_style = S("Title2",
    fontSize=22, fontName="Helvetica-Bold",
    textColor=WHITE, alignment=TA_CENTER, spaceAfter=4)

subtitle_style = S("Subtitle2",
    fontSize=11, fontName="Helvetica",
    textColor=colors.HexColor("#D5E8D4"), alignment=TA_CENTER, spaceAfter=2)

h1 = S("H1",
    fontSize=13, fontName="Helvetica-Bold",
    textColor=WHITE, alignment=TA_LEFT, spaceAfter=0)

h2 = S("H2",
    fontSize=11, fontName="Helvetica-Bold",
    textColor=TEAL, spaceBefore=8, spaceAfter=3)

h3 = S("H3",
    fontSize=10, fontName="Helvetica-Bold",
    textColor=GREY_DARK, spaceBefore=4, spaceAfter=2)

body = S("Body2",
    fontSize=9, fontName="Helvetica",
    textColor=GREY_DARK, spaceAfter=2, leading=13)

body_bold = S("BodyBold",
    fontSize=9, fontName="Helvetica-Bold",
    textColor=GREY_DARK, spaceAfter=2, leading=13)

bullet_style = S("Bullet2",
    fontSize=9, fontName="Helvetica",
    textColor=GREY_DARK, leftIndent=10, spaceAfter=1,
    bulletIndent=0, leading=13)

small = S("Small",
    fontSize=8, fontName="Helvetica",
    textColor=GREY_MID, alignment=TA_CENTER, spaceAfter=0)

tip_style = S("Tip",
    fontSize=9, fontName="Helvetica-Bold",
    textColor=ORANGE, spaceAfter=2, leading=13)

# ── Helper: section header banner ────────────────────────────────
def section_banner(text, bg=TEAL):
    data = [[Paragraph(text, h1)]]
    t = Table(data, colWidths=[17*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND",  (0,0), (-1,-1), bg),
        ("TOPPADDING",  (0,0), (-1,-1), 6),
        ("BOTTOMPADDING",(0,0),(-1,-1), 6),
        ("LEFTPADDING", (0,0), (-1,-1), 10),
        ("RIGHTPADDING",(0,0), (-1,-1), 10),
        ("ROUNDEDCORNERS", [4]),
    ]))
    return t

def tip_box(text, bg=ORANGE_LT, border=ORANGE):
    data = [[Paragraph(text, S("tipinner", fontSize=9, fontName="Helvetica",
                               textColor=GREY_DARK, leading=13))]]
    t = Table(data, colWidths=[17*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND",    (0,0), (-1,-1), bg),
        ("TOPPADDING",    (0,0), (-1,-1), 6),
        ("BOTTOMPADDING", (0,0), (-1,-1), 6),
        ("LEFTPADDING",   (0,0), (-1,-1), 10),
        ("RIGHTPADDING",  (0,0), (-1,-1), 10),
        ("BOX",           (0,0), (-1,-1), 1.5, border),
        ("ROUNDEDCORNERS",[4]),
    ]))
    return t

def colored_table(data, col_widths, header_bg=TEAL, alt_bg=TEAL_LIGHT):
    t = Table(data, colWidths=col_widths)
    style = [
        ("BACKGROUND",    (0,0), (-1,0), header_bg),
        ("TEXTCOLOR",     (0,0), (-1,0), WHITE),
        ("FONTNAME",      (0,0), (-1,0), "Helvetica-Bold"),
        ("FONTSIZE",      (0,0), (-1,-1), 8.5),
        ("TOPPADDING",    (0,0), (-1,-1), 5),
        ("BOTTOMPADDING", (0,0), (-1,-1), 5),
        ("LEFTPADDING",   (0,0), (-1,-1), 6),
        ("RIGHTPADDING",  (0,0), (-1,-1), 6),
        ("GRID",          (0,0), (-1,-1), 0.5, colors.HexColor("#BDC3C7")),
        ("VALIGN",        (0,0), (-1,-1), "MIDDLE"),
        ("ROWBACKGROUNDS",(0,1), (-1,-1), [WHITE, alt_bg]),
    ]
    t.setStyle(TableStyle(style))
    return t

def sp(n=1):
    return Spacer(1, n*3*mm)

# ══════════════════════════════════════════════════════════════════
#  BUILD DOCUMENT
# ══════════════════════════════════════════════════════════════════
doc = SimpleDocTemplate(
    OUTPUT,
    pagesize=A4,
    leftMargin=2*cm, rightMargin=2*cm,
    topMargin=1.5*cm, bottomMargin=2*cm,
    title="Assessment of Nutritional Status - PSM Quick Reference",
    author="Orris Medical"
)

story = []

# ── TITLE BLOCK ──────────────────────────────────────────────────
title_data = [[
    Paragraph("ASSESSMENT OF NUTRITIONAL STATUS", title_style),
    ],
    [Paragraph("PSM Quick Reference  |  Park's Textbook  |  Exam Edition", subtitle_style)]
]
title_tbl = Table(title_data, colWidths=[17*cm])
title_tbl.setStyle(TableStyle([
    ("BACKGROUND",    (0,0), (-1,-1), TEAL),
    ("TOPPADDING",    (0,0), (-1,0),  14),
    ("BOTTOMPADDING", (0,0), (-1,-1), 10),
    ("LEFTPADDING",   (0,0), (-1,-1), 14),
    ("RIGHTPADDING",  (0,0), (-1,-1), 14),
    ("ROUNDEDCORNERS",[6]),
]))
story.append(title_tbl)
story.append(sp(2))

# ── INTRO DEFINITION ─────────────────────────────────────────────
story.append(tip_box(
    "<b>Definition:</b> Nutritional status is the result of many interrelated factors - "
    "adequacy of food intake (quantity + quality) and physical health of the individual. "
    "The <b>purpose</b> of nutritional assessment is to identify at-risk groups, develop health "
    "care programmes, and evaluate their effectiveness. <i>(Park's PSM)</i>",
    bg=TEAL_LIGHT, border=TEAL
))
story.append(sp(2))

# ── 7 METHODS OVERVIEW TABLE ─────────────────────────────────────
story.append(section_banner("7 METHODS OF NUTRITIONAL ASSESSMENT  [Memory: C-A-B-F-D-V-E]"))
story.append(sp(1))

methods_data = [
    ["#", "Method", "What it Detects", "Best Used For"],
    ["1", "Clinical Examination",     "Obvious/frank deficiency signs",     "Quick field surveys"],
    ["2", "Anthropometry",            "Growth retardation, obesity",         "Children & adults"],
    ["3", "Biochemical Evaluation",   "Subclinical (latent) deficiency",    "Early/lab detection"],
    ["4", "Functional Assessment",    "Functional consequences",             "Work/immune capacity"],
    ["5", "Dietary Intake",           "Inadequate food intake",             "Cause identification"],
    ["6", "Vital & Health Statistics","Population-level trends",            "Community surveys"],
    ["7", "Ecological Studies",       "Environmental/socio risk factors",   "Programme planning"],
]
story.append(colored_table(methods_data,
    col_widths=[1*cm, 4.2*cm, 5.8*cm, 6*cm]))
story.append(sp(1))
story.append(tip_box(
    "⭐ Key Point: These methods are NOT mutually exclusive - they are COMPLEMENTARY to each other.",
    bg=ORANGE_LT, border=ORANGE))
story.append(sp(2))

# ── 1. CLINICAL ──────────────────────────────────────────────────
story.append(section_banner("1. CLINICAL EXAMINATION", bg=PURPLE))
story.append(sp(1))
story.append(Paragraph(
    "Simplest and most practical method. Looks for physical signs of malnutrition. "
    "Signs may be <b>specific</b> (e.g., Bitot's spots = Vit A) or <b>non-specific</b>. "
    "Diagnostic value increases when 2+ signs of same deficiency appear together.",
    body))
story.append(sp(1))

clinical_data = [
    ["WHO Group", "Description", "Examples"],
    ["Group 1\n(Definite signs)",
     "Strongly associated\nwith malnutrition",
     "Bitot's spots, corneal xerosis,\nfollicular keratosis,\nangular stomatitis, oedema,\nscrotal/vulval dermatosis"],
    ["Group 2\n(Possible signs)",
     "May or may not be\ndue to malnutrition",
     "Cheilosis (swollen lips),\nthyroid enlargement,\nconjunctival pallor,\ndental caries"],
    ["Group 3\n(Non-specific signs)",
     "Not reliably associated\nwith malnutrition",
     "Nasal discharge,\nskin infections"],
]
story.append(colored_table(clinical_data,
    col_widths=[3.5*cm, 5.5*cm, 8*cm], header_bg=PURPLE, alt_bg=PURPLE_LT))
story.append(sp(2))

# ── 2. ANTHROPOMETRY ─────────────────────────────────────────────
story.append(section_banner("2. ANTHROPOMETRY", bg=GREEN))
story.append(sp(1))
story.append(Paragraph(
    "Measurement of body dimensions to assess nutritional status. Most useful in children.",
    body))
story.append(sp(1))

# Key Indices
story.append(Paragraph("KEY ANTHROPOMETRIC INDICES", h3))
anthro_data = [
    ["Index", "Detects", "Condition"],
    ["Weight for Age (W/A)",   "Overall nutrition",    "Underweight"],
    ["Height for Age (H/A)",   "Chronic malnutrition", "Stunting"],
    ["Weight for Height (W/H)","Acute malnutrition",   "Wasting"],
    ["BMI = Wt(kg)/Ht²(m)",   "Obesity / thinness",   "Used in adults"],
    ["MUAC",                   "Arm muscle/fat stores","Children (age unknown OK)"],
    ["Skinfold Thickness",     "Body fat",             "Triceps, subscapular site"],
]
story.append(colored_table(anthro_data,
    col_widths=[5*cm, 5*cm, 7*cm], header_bg=GREEN, alt_bg=GREEN_LT))
story.append(sp(1))

# BMI + MUAC side by side
bmi_data = [
    ["BMI Cut-offs (Adults)", ""],
    ["< 18.5", "Underweight (CED)"],
    ["18.5 - 24.9", "Normal"],
    ["25.0 - 29.9", "Overweight"],
    ["≥ 30.0", "Obese"],
]
muac_data = [
    ["MUAC Cut-offs (Children 1-5 yrs)", ""],
    ["< 12.5 cm", "Severe malnutrition"],
    ["12.5 - 13.5 cm", "Moderate malnutrition"],
    ["> 13.5 cm", "Normal"],
    ["", ""],
]

def small_table(data, header_bg, alt_bg, col_w):
    t = Table(data, colWidths=col_w)
    t.setStyle(TableStyle([
        ("BACKGROUND",    (0,0), (-1,0), header_bg),
        ("SPAN",          (0,0), (-1,0)),
        ("TEXTCOLOR",     (0,0), (-1,0), WHITE),
        ("FONTNAME",      (0,0), (-1,0), "Helvetica-Bold"),
        ("FONTSIZE",      (0,0), (-1,-1), 8.5),
        ("TOPPADDING",    (0,0), (-1,-1), 4),
        ("BOTTOMPADDING", (0,0), (-1,-1), 4),
        ("LEFTPADDING",   (0,0), (-1,-1), 6),
        ("RIGHTPADDING",  (0,0), (-1,-1), 6),
        ("GRID",          (0,0), (-1,-1), 0.5, colors.HexColor("#BDC3C7")),
        ("ROWBACKGROUNDS",(0,1), (-1,-1), [WHITE, alt_bg]),
        ("ALIGN",         (0,0), (-1,0), "CENTER"),
        ("VALIGN",        (0,0), (-1,-1), "MIDDLE"),
    ]))
    return t

side_data = [[
    small_table(bmi_data, GREEN, GREEN_LT, [3.5*cm, 3*cm]),
    Spacer(0.5*cm, 1),
    small_table(muac_data, TEAL, TEAL_LIGHT, [3.8*cm, 3.2*cm]),
]]
side_tbl = Table(side_data, colWidths=[6.5*cm, 0.5*cm, 10*cm])
side_tbl.setStyle(TableStyle([
    ("VALIGN", (0,0), (-1,-1), "TOP"),
    ("TOPPADDING", (0,0), (-1,-1), 0),
    ("BOTTOMPADDING", (0,0), (-1,-1), 0),
    ("LEFTPADDING", (0,0), (-1,-1), 0),
    ("RIGHTPADDING", (0,0), (-1,-1), 0),
]))
story.append(side_tbl)
story.append(sp(1))

# Gomez Classification
story.append(Paragraph("GOMEZ CLASSIFICATION (PEM Grading - Weight for Age)", h3))
gomez_data = [
    ["Grade", "% of Standard Weight", "Severity"],
    ["Grade I",   "75 - 90%",  "Mild PEM"],
    ["Grade II",  "60 - 74%",  "Moderate PEM"],
    ["Grade III", "< 60%",     "Severe PEM"],
]
story.append(colored_table(gomez_data,
    col_widths=[4*cm, 6*cm, 7*cm], header_bg=GREEN, alt_bg=GREEN_LT))
story.append(sp(2))

# ── 3. BIOCHEMICAL ───────────────────────────────────────────────
story.append(section_banner("3. BIOCHEMICAL EVALUATION", bg=RED))
story.append(sp(1))
story.append(tip_box(
    "⭐ MOST SENSITIVE method - detects SUBCLINICAL (latent) deficiency BEFORE clinical signs appear!",
    bg=colors.HexColor("#FDEDEC"), border=RED))
story.append(sp(1))

bio_data = [
    ["Nutrient / Parameter", "Test Used"],
    ["Protein status",           "Serum albumin, transferrin, prealbumin"],
    ["Iron deficiency anaemia",  "Haemoglobin (Hb), serum ferritin, serum iron, TIBC"],
    ["Vitamin A deficiency",     "Serum retinol level"],
    ["Iodine deficiency",        "Urinary iodine excretion (UIE)"],
    ["Vitamin D deficiency",     "Serum 25-OH Vitamin D"],
    ["Vitamin C deficiency",     "Serum / urine ascorbic acid"],
    ["Overall nutrition",        "Serum albumin (< 3.5 g/dL = malnutrition)"],
]
story.append(colored_table(bio_data,
    col_widths=[6*cm, 11*cm], header_bg=RED,
    alt_bg=colors.HexColor("#FDEDEC")))
story.append(sp(2))

# ── 4. FUNCTIONAL ────────────────────────────────────────────────
story.append(section_banner("4. FUNCTIONAL ASSESSMENT", bg=ORANGE))
story.append(sp(1))
story.append(Paragraph(
    "Assesses functional consequences of malnutrition:", body))

func_items = [
    "Immune function - delayed hypersensitivity skin test",
    "Muscle function - hand grip strength (dynamometer)",
    "Work capacity / physical performance tests",
    "Cognitive function - mental development in children",
    "Dark adaptation test - for Vitamin A deficiency",
]
for item in func_items:
    story.append(Paragraph(f"• {item}", bullet_style))
story.append(sp(2))

# ── 5. DIETARY INTAKE ────────────────────────────────────────────
story.append(section_banner("5. ASSESSMENT OF DIETARY INTAKE", bg=colors.HexColor("#1A5276")))
story.append(sp(1))
story.append(Paragraph(
    "Measures what food the person actually eats and compares with RDA "
    "(Recommended Dietary Allowance).", body))
story.append(sp(1))

diet_data = [
    ["Method", "Description"],
    ["24-Hour Recall",        "Patient recalls everything eaten in past 24 hrs - MOST commonly used"],
    ["Dietary History",       "Long-term usual diet pattern (3-day or 7-day recall)"],
    ["Food Frequency Questionnaire", "How often certain foods are eaten per week/month"],
    ["Weighed Food Record",   "Actual weighing of food before consumption - most accurate"],
    ["Food Balance Sheets",   "National food availability data (FAO method)"],
]
story.append(colored_table(diet_data,
    col_widths=[5.5*cm, 11.5*cm],
    header_bg=colors.HexColor("#1A5276"),
    alt_bg=colors.HexColor("#EAF2FF")))
story.append(sp(2))

# ── 6 & 7 side by side ───────────────────────────────────────────
story.append(section_banner("6 & 7. VITAL STATISTICS  +  ECOLOGICAL STUDIES", bg=GREY_DARK))
story.append(sp(1))

vital_items = [
    "IMR (Infant Mortality Rate) - sensitive indicator",
    "Child mortality rate (1-4 years = toddler mortality)",
    "Birth weight (< 2500 g = low birth weight)",
    "Prevalence of stunting/wasting in community",
    "Morbidity data (disease frequency)",
]
eco_items = [
    "Food production & agricultural data",
    "Socioeconomic factors (income, poverty)",
    "Food availability and distribution patterns",
    "Education and literacy levels",
    "Cultural food habits and beliefs",
]

def bullet_cell(title, items, bg):
    content = [Paragraph(title, S("ct", fontSize=9, fontName="Helvetica-Bold",
                                   textColor=WHITE))]
    for it in items:
        content.append(Paragraph(f"• {it}", S("bi", fontSize=8.5, fontName="Helvetica",
                                               textColor=GREY_DARK, leftIndent=6, leading=12)))
    data = [[content]]
    t = Table(data, colWidths=[8*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND",    (0,0), (0,0), bg),
        ("BACKGROUND",    (0,1), (0,-1), GREY_LIGHT),
        ("TOPPADDING",    (0,0), (-1,-1), 5),
        ("BOTTOMPADDING", (0,0), (-1,-1), 5),
        ("LEFTPADDING",   (0,0), (-1,-1), 8),
        ("BOX",           (0,0), (-1,-1), 0.5, GREY_MID),
    ]))
    return t

v_tbl = small_table(
    [["6. VITAL & HEALTH STATISTICS"]] +
    [[f"• {i}"] for i in vital_items],
    header_bg=GREY_DARK, alt_bg=GREY_LIGHT, col_w=[8.2*cm]
)
e_tbl = small_table(
    [["7. ECOLOGICAL STUDIES"]] +
    [[f"• {i}"] for i in eco_items],
    header_bg=GREY_DARK, alt_bg=GREY_LIGHT, col_w=[8.2*cm]
)

dual = Table([[v_tbl, Spacer(0.6*cm, 1), e_tbl]],
             colWidths=[8.2*cm, 0.6*cm, 8.2*cm])
dual.setStyle(TableStyle([
    ("VALIGN", (0,0), (-1,-1), "TOP"),
    ("TOPPADDING", (0,0), (-1,-1), 0),
    ("BOTTOMPADDING", (0,0), (-1,-1), 0),
    ("LEFTPADDING", (0,0), (-1,-1), 0),
    ("RIGHTPADDING", (0,0), (-1,-1), 0),
]))
story.append(dual)
story.append(sp(2))

# ── NUTRITIONAL SURVEILLANCE ─────────────────────────────────────
story.append(section_banner("NUTRITIONAL SURVEILLANCE", bg=PURPLE))
story.append(sp(1))
story.append(Paragraph(
    "<b>Definition:</b> Continuous monitoring of nutritional status of a population over time "
    "to detect changes and facilitate timely action.", body))
story.append(sp(1))

surv_data = [
    ["Population Group", "Key Indicator(s)"],
    ["Maternal nutrition",         "Birth weight"],
    ["Infants & preschool (age known)",   "Height for age, Weight for age"],
    ["Infants & preschool (age unknown)", "Weight for height, MUAC, clinical signs"],
    ["School children",           "Height for age + Weight for height at age 7"],
]
story.append(colored_table(surv_data,
    col_widths=[7*cm, 10*cm], header_bg=PURPLE, alt_bg=PURPLE_LT))
story.append(sp(1))
story.append(tip_box(
    "<b>Assessment vs Surveillance:</b>  Assessment = done at ONE point in time (cross-sectional).  "
    "Surveillance = done CONTINUOUSLY over time (ongoing monitoring).",
    bg=ORANGE_LT, border=ORANGE))
story.append(sp(2))

# ── EXAM QUICK RECALL ────────────────────────────────────────────
story.append(section_banner("EXAM QUICK RECALL - IMPORTANT POINTS", bg=RED))
story.append(sp(1))

recall_items = [
    ("<b>Simplest & most practical</b> method", "Clinical Examination"),
    ("<b>Most sensitive</b> method (detects subclinical)", "Biochemical Evaluation"),
    ("<b>Wasting</b> = low Weight for Height", "Acute malnutrition"),
    ("<b>Stunting</b> = low Height for Age", "Chronic malnutrition"),
    ("<b>Underweight</b> = low Weight for Age", "Composite/overall"),
    ("<b>Best when age is unknown</b>", "MUAC or Weight for Height"),
    ("<b>Gomez Grade III</b> (Severe PEM)", "Weight < 60% of standard"),
    ("<b>Low Birth Weight</b>", "< 2500 g"),
    ("<b>Severe MUAC in children</b>", "< 12.5 cm"),
    ("<b>Serum albumin cut-off for malnutrition</b>", "< 3.5 g/dL"),
]

recall_data = [["Key Fact", "Answer"]] + recall_items
story.append(colored_table(recall_data,
    col_widths=[9*cm, 8*cm],
    header_bg=RED, alt_bg=colors.HexColor("#FDEDEC")))
story.append(sp(2))

# ── MEMORY AID ───────────────────────────────────────────────────
story.append(tip_box(
    "🧠  <b>MEMORY AID:</b>  C - A - B - F - D - V - E  "
    "(Clinical | Anthropometry | Biochemical | Functional | Dietary | Vital stats | Ecological)\n"
    "Think: <b>\"Can A Boy Find Directions Via Ecology?\"</b>",
    bg=GREEN_LT, border=GREEN))
story.append(sp(1))
story.append(Paragraph(
    "Source: Park's Textbook of Preventive and Social Medicine  |  Generated by Orris Medical",
    small))

# ── BUILD ─────────────────────────────────────────────────────────
doc.build(story)
print(f"PDF created: {OUTPUT}")

Running Command

cd /home/daytona/workspace/nutrition-psm && python3 generate_pdf.py

Your PDF is ready! Download it here:

Nutritional Status Assessment PSM

PDF Document · PDF

Here's what's packed inside:

Color-coded sections for each of the 7 methods (Clinical, Anthropometry, Biochemical, Functional, Dietary, Vital Stats, Ecological)
WHO classification table for clinical signs (3 groups)
Anthropometric indices - Wasting / Stunting / Underweight explained clearly
BMI + MUAC cut-off tables side by side
Gomez Classification table for PEM grading
Biochemical tests - nutrient by nutrient
Dietary intake methods table
Nutritional surveillance indicators (Assessment vs Surveillance difference)
Exam Quick Recall table - 10 most commonly asked facts
Memory aid at the bottom: "Can A Boy Find Directions Via Ecology?"

Print it, stick it somewhere visible, and revise tonight. All the best for your exam tomorrow!

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