what is more likely to come for medical university exam for the topic cerebellum

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cerebellum high yield topics medical university exam MBBS anatomy physiology

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I now have everything needed for a thorough, exam-focused answer. Here is the complete high-yield breakdown:

High-Yield Cerebellum Topics for Medical University Exams

Based on authoritative textbooks (Ganong's, Costanzo, Neuroanatomy through Clinical Cases), here is what examiners consistently test - organized from most to least likely to appear.

1. FUNCTIONAL DIVISIONS (Most Frequently Tested)

There are three functional divisions, and knowing which controls what is a guaranteed exam question:

Division	Anatomical Region	Primary Input	Function
Vestibulocerebellum	Flocculonodular lobe	Vestibular (labyrinth)	Balance, eye movements
Spinocerebellum	Vermis + intermediate hemispheres	Spinal cord	Synergy/coordination of ongoing movement
Pontocerebellum (Cerebrocerebellum)	Lateral hemispheres	Cerebral cortex via pontine nuclei	Planning and initiation of movement

Mnemonic: VeStibulocerebellum = Vestibular/balance; SPinocerebellum = SPindle/movement synergy; PonTOcerebellum = Planning/Timing.

2. THREE CEREBELLAR PEDUNCLES (Very High Yield)

Peduncle	Direction	Key Contents
Superior	Mainly EFFERENT	Output to red nucleus and thalamus (dentato-rubro-thalamic tract)
Middle	Only AFFERENT	From contralateral pontine nuclei (corticopontocerebellar tract)
Inferior	Mixed	Afferent from spinal cord + brainstem; efferent to vestibular nuclei

Exam pearl: The middle cerebellar peduncle is the largest and carries only afferents. The superior cerebellar peduncle is the main output route.

3. LAYERS OF THE CEREBELLAR CORTEX (Extremely High Yield)

Cerebellar cortex layers with five cell types - Costanzo Physiology

Three layers (from inside out):

Layer	Contents	Key Features
Granular (innermost)	Granule cells, Golgi II cells, glomeruli	Mossy fibers synapse here on granule cell dendrites in glomeruli
Purkinje cell (middle)	Purkinje cells only	One cell thick; output is ALWAYS inhibitory (GABA)
Molecular (outermost)	Basket cells, stellate cells, parallel fibers, Purkinje dendrites	Parallel fibers run here

Five cell types of cerebellar cortex - Ganong's

4. FIVE CELL TYPES - EXCITATORY vs INHIBITORY (Guaranteed MCQ)

Cell	Location	Action
Granule cell	Granular layer	EXCITATORY (glutamate) - the ONLY excitatory interneuron
Purkinje cell	Purkinje layer	INHIBITORY (GABA) - sole OUTPUT of cerebellar cortex
Basket cell	Molecular layer	INHIBITORY (GABA) - inhibits Purkinje cells
Stellate cell	Molecular layer	INHIBITORY (GABA) - inhibits Purkinje cell dendrites
Golgi II cell	Granular layer	INHIBITORY (GABA) - inhibits granule cells (feedback)

Exam pearl: Among all 5 cell types, only granule cells are excitatory. All others (Purkinje, basket, stellate, Golgi) are inhibitory. The Purkinje cell axon is the only output of the cerebellar cortex.

5. AFFERENT FIBER SYSTEMS (High Yield)

Two input systems - both excitatory:

Climbing fibers:

Origin: Inferior olivary nucleus of medulla
Project directly onto Purkinje cell dendrites
Each Purkinje cell receives input from only ONE climbing fiber
Produce complex spikes in Purkinje cells
Role in motor learning

Mossy fibers:

Origin: Everything else (vestibulocerebellar, spinocerebellar, pontocerebellar)
Synapse on granule cells in glomeruli
Granule cells then send parallel fibers to Purkinje cells
One Purkinje cell can receive up to 250,000 parallel fibers
Produce simple spikes in Purkinje cells

6. DEEP CEREBELLAR NUCLEI (Medium-High Yield)

From medial to lateral: "Don't Eat Greasy Food"

Fastigial (most medial) - receives from vermis, projects to vestibular nuclei
Globose - interpositus group
Emboliform - interpositus group
Dentate (most lateral) - receives from lateral hemispheres, projects via superior peduncle to red nucleus and thalamus (main output)

Key rule: Output of deep cerebellar nuclei to thalamus and brainstem is always EXCITATORY, despite receiving inhibitory Purkinje cell input.

7. SIGNS OF CEREBELLAR DISEASE (Very High Yield for Clinical Exams)

Ataxia is IPSILATERAL to the lesion (unlike cortical lesions which are contralateral).

Sign	Description
Dysmetria	Overshooting/undershooting a target
Intention tremor	Tremor that worsens as limb approaches target (vs. resting tremor in Parkinson's)
Dysdiadochokinesia	Inability to perform rapid alternating movements (e.g., pronation-supination)
Rebound phenomenon	Cannot arrest a movement; limb swings past when resistance is removed
Decomposition of movement	Complex movements are broken into segments, joint by joint
Nystagmus	Especially with flocculonodular lesions
Scanning (staccato) speech	Slurred, irregular speech rhythm
Truncal ataxia + wide-based gait	Vermis lesions

8. LOCALIZATION OF CEREBELLAR LESIONS (Classic Exam Scenario)

Flocculonodular lobe lesion (vestibulocerebellum): truncal ataxia, wide-based gait, nystagmus, vertigo, nausea/vomiting - NO limb ataxia
Vermis lesion: truncal/gait ataxia predominantly
Lateral hemisphere lesion: ipsilateral limb ataxia (appendicular ataxia), intention tremor, dysdiadochokinesia
Cerebellar hemisphere in a child: think medulloblastoma (most common pediatric brain tumor)

9. CEREBELLAR AFFERENT TRACTS (Exam Table - Ganong's)

Tract	Carries
Vestibulocerebellar	Vestibular impulses from labyrinths
Dorsal spinocerebellar	Proprioception from lower limbs (Golgi tendon organs, muscle spindles)
Ventral spinocerebellar	Proprioception from upper and lower limbs
Cuneocerebellar	Proprioception from upper limb and upper thorax
Pontocerebellar	Motor commands from cerebral cortex via pontine nuclei
Olivocerebellar	Whole-body proprioception via inferior olive - travels as climbing fibers

10. MOTOR LEARNING ROLE (Short-Answer Favorite)

The cerebellum is involved in procedural/motor learning. Climbing fibers from the inferior olive increase activity when a new movement is being learned. Long-term depression (LTD) at the mossy fiber-Purkinje cell synapse is the proposed cellular mechanism of motor learning. Selective lesions of the olivary complex abolish this learning capacity.

Quick Revision Summary (What to memorize first)

Vestibulocerebellum = balance/eye movements | Spinocerebellum = coordination | Pontocerebellum = planning
Three layers: Granular (inner) - Purkinje (middle) - Molecular (outer)
Only granule cell is excitatory; Purkinje cell = sole inhibitory output (GABA)
Climbing fibers = inferior olive → Purkinje directly (complex spikes, motor learning)
Mossy fibers → granule cells → parallel fibers → Purkinje (simple spikes)
Middle peduncle = only afferent, largest; Superior peduncle = main efferent
Cerebellar signs are IPSILATERAL; intention tremor (not resting)
Deep nuclei lateral to medial: Dentate, Emboliform, Globose, Fastigial

Sources: Costanzo Physiology 7th Edition, pp. 113-115 | Ganong's Review of Medical Physiology 26th Edition, pp. 253-258 | Neuroanatomy through Clinical Cases 3rd Edition, pp. 725-730

Generate a one-page study cheat sheet PDF for cerebellar anatomy

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Running Command

ls -la /tmp/workspace/ 2>/dev/null || mkdir -p /tmp/workspace && ls -la /tmp/workspace/

Running Command

mkdir -p /tmp/workspace/cerebellum-cheatsheet

Writing File

~/cerebellum-cheatsheet/generate_cheatsheet.py

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

OUTPUT = "/tmp/workspace/cerebellum-cheatsheet/Cerebellum_Cheatsheet.pdf"

# ── Colour palette ────────────────────────────────────────────────────────────
C_NAVY    = HexColor("#1B3A5C")
C_TEAL    = HexColor("#1E7F8E")
C_AMBER   = HexColor("#E8A020")
C_ROSE    = HexColor("#C0392B")
C_GREEN   = HexColor("#1E7F4E")
C_LGRAY   = HexColor("#F2F5F8")
C_MGRAY   = HexColor("#D6DCE4")
C_WHITE   = colors.white
C_BLACK   = colors.black

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

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

title_style = S("TitleStyle",
    fontSize=20, fontName="Helvetica-Bold",
    textColor=C_WHITE, alignment=TA_CENTER,
    spaceAfter=2)

subtitle_style = S("SubtitleStyle",
    fontSize=9, fontName="Helvetica",
    textColor=HexColor("#CCE4EE"), alignment=TA_CENTER,
    spaceAfter=0)

sec_style = S("SecStyle",
    fontSize=8.5, fontName="Helvetica-Bold",
    textColor=C_WHITE, alignment=TA_LEFT,
    leftPadding=4, spaceBefore=1, spaceAfter=1)

body_style = S("BodyStyle",
    fontSize=7.2, fontName="Helvetica",
    textColor=C_BLACK, leading=10,
    leftPadding=0, spaceBefore=0)

body_bold = S("BodyBold",
    fontSize=7.2, fontName="Helvetica-Bold",
    textColor=C_NAVY, leading=10)

small_style = S("SmallStyle",
    fontSize=6.5, fontName="Helvetica",
    textColor=HexColor("#444444"), leading=9)

mnemonic_style = S("MnemonicStyle",
    fontSize=7.4, fontName="Helvetica-BoldOblique",
    textColor=C_ROSE, leading=10,
    backColor=HexColor("#FFF4E5"),
    borderPadding=(2,4,2,4))

pearl_style = S("PearlStyle",
    fontSize=7.0, fontName="Helvetica-Oblique",
    textColor=HexColor("#1B3A5C"), leading=10,
    backColor=HexColor("#EAF4FF"),
    borderPadding=(2,4,2,4))

# ── Helper: coloured section header ──────────────────────────────────────────
def sec_header(text, color=C_NAVY):
    data = [[Paragraph(f"<b>{text}</b>", S("sh",
        fontSize=8, fontName="Helvetica-Bold", textColor=C_WHITE,
        alignment=TA_LEFT))]]
    t = Table(data, colWidths=["100%"])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), color),
        ("TOPPADDING",   (0,0), (-1,-1), 3),
        ("BOTTOMPADDING",(0,0), (-1,-1), 3),
        ("LEFTPADDING",  (0,0), (-1,-1), 6),
    ]))
    return t

def P(txt, style=body_style): return Paragraph(txt, style)
def SP(h=2): return Spacer(1, h)

# ── Build content ─────────────────────────────────────────────────────────────
story = []

# ─── TITLE BANNER ─────────────────────────────────────────────────────────────
banner_data = [[
    Paragraph("CEREBELLUM", S("T2", fontSize=22, fontName="Helvetica-Bold",
        textColor=C_WHITE, alignment=TA_CENTER)),
    Paragraph("Anatomy &amp; Physiology · Exam Cheat Sheet",
        S("T3", fontSize=9, fontName="Helvetica", textColor=HexColor("#CCE4EE"),
        alignment=TA_CENTER))
]]
banner = Table([[
    Paragraph("🧠  CEREBELLUM  —  Anatomy &amp; Physiology Cheat Sheet",
        S("tb", fontSize=15, fontName="Helvetica-Bold",
        textColor=C_WHITE, alignment=TA_CENTER))
]], colWidths=[190*mm])
banner.setStyle(TableStyle([
    ("BACKGROUND", (0,0), (-1,-1), C_NAVY),
    ("TOPPADDING",   (0,0), (-1,-1), 8),
    ("BOTTOMPADDING",(0,0), (-1,-1), 8),
    ("LEFTPADDING",  (0,0), (-1,-1), 6),
    ("ROUNDEDCORNERS", [4, 4, 4, 4]),
]))
story.append(banner)
story.append(SP(5))

# ─── ROW 1: Functional Divisions | Peduncles ──────────────────────────────────
# Left: Functional Divisions
div_header = sec_header("⚡ FUNCTIONAL DIVISIONS", C_TEAL)
div_data = [
    [P("<b>Division</b>", body_bold), P("<b>Region</b>", body_bold), P("<b>Input</b>", body_bold), P("<b>Function</b>", body_bold)],
    [P("Vestibulocerebellum", body_style), P("Flocculonodular lobe", small_style), P("Vestibular (labyrinth)", small_style), P("Balance, eye movements", small_style)],
    [P("Spinocerebellum", body_style), P("Vermis + intermediate", small_style), P("Spinal cord", small_style), P("Synergy of ongoing movement", small_style)],
    [P("Pontocerebellum", body_style), P("Lateral hemispheres", small_style), P("Cerebral cortex → pons", small_style), P("Planning &amp; initiation", small_style)],
]
div_table = Table(div_data, colWidths=[28*mm, 30*mm, 34*mm, 36*mm])
div_table.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0), C_MGRAY),
    ("BACKGROUND",   (0,1), (-1,1), C_LGRAY),
    ("BACKGROUND",   (0,2), (-1,2), C_WHITE),
    ("BACKGROUND",   (0,3), (-1,3), C_LGRAY),
    ("GRID",         (0,0), (-1,-1), 0.4, C_MGRAY),
    ("TOPPADDING",   (0,0), (-1,-1), 2),
    ("BOTTOMPADDING",(0,0), (-1,-1), 2),
    ("LEFTPADDING",  (0,0), (-1,-1), 3),
    ("VALIGN",       (0,0), (-1,-1), "MIDDLE"),
]))

# Right: Peduncles
ped_header = sec_header("🔗 CEREBELLAR PEDUNCLES", C_ROSE)
ped_data = [
    [P("<b>Peduncle</b>", body_bold), P("<b>Direction</b>", body_bold), P("<b>Key Contents</b>", body_bold)],
    [P("Superior (SCP)", body_style), P("<font color='#C0392B'>Mainly EFFERENT</font>", small_style), P("Dentato-rubro-thalamic tract", small_style)],
    [P("Middle (MCP)", body_style), P("<font color='#1E7F8E'>Only AFFERENT</font>", small_style), P("Corticopontocerebellar (LARGEST)", small_style)],
    [P("Inferior (ICP)", body_style), P("Mixed", small_style), P("Spinocerebellar, vestibular", small_style)],
]
ped_table = Table(ped_data, colWidths=[26*mm, 24*mm, 40*mm])
ped_table.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0), C_MGRAY),
    ("BACKGROUND",   (0,1), (-1,1), C_LGRAY),
    ("BACKGROUND",   (0,2), (-1,2), C_WHITE),
    ("BACKGROUND",   (0,3), (-1,3), C_LGRAY),
    ("GRID",         (0,0), (-1,-1), 0.4, C_MGRAY),
    ("TOPPADDING",   (0,0), (-1,-1), 2),
    ("BOTTOMPADDING",(0,0), (-1,-1), 2),
    ("LEFTPADDING",  (0,0), (-1,-1), 3),
    ("VALIGN",       (0,0), (-1,-1), "MIDDLE"),
]))

row1 = Table([
    [div_header, SP(3), ped_header],
    [div_table,  SP(3), ped_table],
], colWidths=[131*mm, 4*mm, 93*mm])
row1.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(row1)
story.append(SP(5))

# ─── ROW 2: Cortical Layers | Cell Types ──────────────────────────────────────
layer_header = sec_header("🔬 CORTICAL LAYERS (outer → inner)", C_GREEN)
layer_data = [
    [P("<b>Layer</b>", body_bold), P("<b>Contents</b>", body_bold), P("<b>Notes</b>", body_bold)],
    [P("Molecular (outer)", body_style), P("Basket cells, stellate cells, parallel fibers, Purkinje dendrites", small_style), P("Parallel fibers run here", small_style)],
    [P("Purkinje cell (middle)", body_style), P("Purkinje cells only", small_style), P("1 cell thick · output ALWAYS inhibitory (GABA)", small_style)],
    [P("Granular (inner)", body_style), P("Granule cells, Golgi II cells, glomeruli", small_style), P("Mossy fibers synapse here; glomeruli = complex synapse clusters", small_style)],
]
layer_table = Table(layer_data, colWidths=[36*mm, 60*mm, 64*mm])
layer_table.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0), C_MGRAY),
    ("BACKGROUND",   (0,1), (-1,1), C_LGRAY),
    ("BACKGROUND",   (0,2), (-1,2), C_WHITE),
    ("BACKGROUND",   (0,3), (-1,3), C_LGRAY),
    ("GRID",         (0,0), (-1,-1), 0.4, C_MGRAY),
    ("TOPPADDING",   (0,0), (-1,-1), 2),
    ("BOTTOMPADDING",(0,0), (-1,-1), 2),
    ("LEFTPADDING",  (0,0), (-1,-1), 3),
    ("VALIGN",       (0,0), (-1,-1), "MIDDLE"),
]))

# Cell types
cell_header = sec_header("🧬 5 CELL TYPES — EXCITATORY vs INHIBITORY", C_AMBER)
cell_data = [
    [P("<b>Cell</b>", body_bold), P("<b>Layer</b>", body_bold), P("<b>Type</b>", body_bold), P("<b>Key Role</b>", body_bold)],
    [P("Granule", body_style),   P("Granular",  small_style), P("<font color='#1E7F4E'><b>EXCIT (Glu)</b></font>", small_style), P("→ parallel fibers → Purkinje", small_style)],
    [P("Purkinje", body_style),  P("Purkinje",  small_style), P("<font color='#C0392B'><b>INHIB (GABA)</b></font>", small_style), P("SOLE OUTPUT of cortex", small_style)],
    [P("Basket",   body_style),  P("Molecular", small_style), P("<font color='#C0392B'><b>INHIB (GABA)</b></font>", small_style), P("Inhibits Purkinje cell soma", small_style)],
    [P("Stellate", body_style),  P("Molecular", small_style), P("<font color='#C0392B'><b>INHIB (GABA)</b></font>", small_style), P("Inhibits Purkinje dendrites", small_style)],
    [P("Golgi II", body_style),  P("Granular",  small_style), P("<font color='#C0392B'><b>INHIB (GABA)</b></font>", small_style), P("Feedback: inhibits granule cells", small_style)],
]
cell_table = Table(cell_data, colWidths=[22*mm, 22*mm, 30*mm, 44*mm])
cell_table.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0), C_MGRAY),
    ("BACKGROUND",   (0,1), (-1,1), HexColor("#E8F8EE")),
    ("BACKGROUND",   (0,2), (-1,2), HexColor("#FFF0EE")),
    ("BACKGROUND",   (0,3), (-1,3), HexColor("#FFF0EE")),
    ("BACKGROUND",   (0,4), (-1,4), HexColor("#FFF0EE")),
    ("BACKGROUND",   (0,5), (-1,5), HexColor("#FFF0EE")),
    ("GRID",         (0,0), (-1,-1), 0.4, C_MGRAY),
    ("TOPPADDING",   (0,0), (-1,-1), 2),
    ("BOTTOMPADDING",(0,0), (-1,-1), 2),
    ("LEFTPADDING",  (0,0), (-1,-1), 3),
    ("VALIGN",       (0,0), (-1,-1), "MIDDLE"),
]))

row2 = Table([
    [layer_header, SP(3), cell_header],
    [layer_table,  SP(3), cell_table],
], colWidths=[163*mm, 4*mm, 61*mm])
row2.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(row2)
story.append(SP(5))

# ─── ROW 3: Afferent Fibers | Deep Nuclei ────────────────────────────────────
fiber_header = sec_header("📥 AFFERENT FIBER SYSTEMS", C_TEAL)
fiber_data = [
    [P("<b>Climbing Fibers</b>", body_bold), P("<b>Mossy Fibers</b>", body_bold)],
    [
        P("Origin: <b>Inferior olivary nucleus</b> (medulla)<br/>"
          "→ Synapse directly on Purkinje cell dendrites<br/>"
          "→ <b>1 climbing fiber per Purkinje cell</b><br/>"
          "→ Produce <b>complex spikes</b><br/>"
          "→ Role in <b>motor learning</b><br/>"
          "→ Activity ↑ when learning a NEW movement", small_style),
        P("Origin: ALL others (spinocerebellar, vestibulocerebellar, pontocerebellar)<br/>"
          "→ Synapse on <b>granule cells</b> in glomeruli<br/>"
          "→ Granule cells → <b>parallel fibers</b> → Purkinje<br/>"
          "→ 1 Purkinje cell receives up to <b>250,000 parallel fibers</b><br/>"
          "→ Produce <b>simple spikes</b><br/>"
          "→ Majority of cerebellar input", small_style),
    ],
]
fiber_table = Table(fiber_data, colWidths=[87*mm, 88*mm])
fiber_table.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0), C_MGRAY),
    ("BACKGROUND",   (0,1), (0,1), HexColor("#EAF4FF")),
    ("BACKGROUND",   (1,1), (1,1), HexColor("#F0FBF5")),
    ("GRID",         (0,0), (-1,-1), 0.4, C_MGRAY),
    ("TOPPADDING",   (0,0), (-1,-1), 3),
    ("BOTTOMPADDING",(0,0), (-1,-1), 3),
    ("LEFTPADDING",  (0,0), (-1,-1), 4),
    ("VALIGN",       (0,0), (-1,-1), "TOP"),
]))

# Deep nuclei
nuc_header = sec_header("🏛 DEEP CEREBELLAR NUCLEI  (lateral → medial)", C_ROSE)
nuc_data = [
    [P("<b>Nucleus</b>", body_bold), P("<b>Receives from</b>", body_bold), P("<b>Projects to</b>", body_bold)],
    [P("<b>Dentate</b>  (most lateral)", small_style), P("Lateral hemispheres", small_style), P("Red nucleus + thalamus via SCP", small_style)],
    [P("Emboliform", small_style), P("Intermed. hemisphere", small_style), P("Red nucleus", small_style)],
    [P("Globose", small_style),    P("Intermed. hemisphere", small_style), P("Red nucleus", small_style)],
    [P("<b>Fastigial</b>  (most medial)", small_style), P("Vermis", small_style), P("Vestibular nuclei via ICP", small_style)],
]
nuc_table = Table(nuc_data, colWidths=[44*mm, 40*mm, 41*mm])
nuc_table.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0), C_MGRAY),
    ("BACKGROUND",   (0,1), (-1,1), HexColor("#FFF0EE")),
    ("BACKGROUND",   (0,2), (-1,2), C_LGRAY),
    ("BACKGROUND",   (0,3), (-1,3), C_LGRAY),
    ("BACKGROUND",   (0,4), (-1,4), HexColor("#FFF0EE")),
    ("GRID",         (0,0), (-1,-1), 0.4, C_MGRAY),
    ("TOPPADDING",   (0,0), (-1,-1), 2),
    ("BOTTOMPADDING",(0,0), (-1,-1), 2),
    ("LEFTPADDING",  (0,0), (-1,-1), 3),
    ("VALIGN",       (0,0), (-1,-1), "MIDDLE"),
]))

row3_top = Table([
    [fiber_header, SP(3), nuc_header],
    [fiber_table,  SP(3), nuc_table],
], colWidths=[178*mm, 4*mm, 128*mm])
row3_top.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(row3_top)
story.append(SP(5))

# ─── ROW 4: Clinical Signs | Lesion Localization | Mnemonics ─────────────────
signs_header = sec_header("⚠ SIGNS OF CEREBELLAR DISEASE", C_ROSE)
signs_data = [
    [P("<b>Sign</b>", body_bold), P("<b>Description</b>", body_bold)],
    [P("Ataxia", small_style),               P("<b>IPSILATERAL</b> to lesion; wide-based unsteady gait", small_style)],
    [P("Dysmetria", small_style),             P("Overshooting/undershooting target", small_style)],
    [P("Intention tremor", small_style),      P("Worsens near target (vs resting tremor in Parkinson's)", small_style)],
    [P("Dysdiadochokinesia", small_style),    P("Cannot do rapid alternating movements", small_style)],
    [P("Rebound phenomenon", small_style),    P("Cannot arrest movement when resistance removed", small_style)],
    [P("Decomposition", small_style),         P("Breaks multi-joint movement into separate joints", small_style)],
    [P("Nystagmus", small_style),             P("Especially flocculonodular lesion", small_style)],
    [P("Scanning speech", small_style),       P("Slurred, staccato, irregular rhythm", small_style)],
]
signs_table = Table(signs_data, colWidths=[34*mm, 70*mm])
signs_table.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0), C_MGRAY),
    *[("BACKGROUND", (0,i), (-1,i), C_LGRAY if i%2==0 else C_WHITE) for i in range(1,9)],
    ("GRID",         (0,0), (-1,-1), 0.4, C_MGRAY),
    ("TOPPADDING",   (0,0), (-1,-1), 2),
    ("BOTTOMPADDING",(0,0), (-1,-1), 2),
    ("LEFTPADDING",  (0,0), (-1,-1), 3),
    ("VALIGN",       (0,0), (-1,-1), "MIDDLE"),
]))

# Localization
loc_header = sec_header("📍 LESION LOCALIZATION", C_GREEN)
loc_data = [
    [P("<b>Lesion Site</b>", body_bold), P("<b>Features</b>", body_bold)],
    [P("Flocculonodular lobe", small_style), P("Truncal ataxia + nystagmus + vertigo/N&amp;V · NO limb ataxia", small_style)],
    [P("Vermis", small_style),               P("Gait/truncal ataxia (proximal muscles)", small_style)],
    [P("Lateral hemisphere", small_style),   P("Ipsilateral limb ataxia, intention tremor, dysdiadochokinesia", small_style)],
    [P("Child + lateral", small_style),      P("Think Medulloblastoma (most common pediatric brain tumour)", small_style)],
]
loc_table = Table(loc_data, colWidths=[34*mm, 72*mm])
loc_table.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0), C_MGRAY),
    ("BACKGROUND",   (0,1), (-1,1), C_LGRAY),
    ("BACKGROUND",   (0,2), (-1,2), C_WHITE),
    ("BACKGROUND",   (0,3), (-1,3), C_LGRAY),
    ("BACKGROUND",   (0,4), (-1,4), HexColor("#FFF0EE")),
    ("GRID",         (0,0), (-1,-1), 0.4, C_MGRAY),
    ("TOPPADDING",   (0,0), (-1,-1), 2),
    ("BOTTOMPADDING",(0,0), (-1,-1), 2),
    ("LEFTPADDING",  (0,0), (-1,-1), 3),
    ("VALIGN",       (0,0), (-1,-1), "MIDDLE"),
]))

# Mnemonics box
mn_header = sec_header("💡 MNEMONICS & PEARLS", C_AMBER)
pearls = [
    ("Only EXCITATORY cell:", "Granule cell (glutamate). All others = GABA inhibitory."),
    ("Purkinje cell output:", "SOLE cortical output · ALWAYS GABA inhibitory → deep nuclei"),
    ("Deep nuclei output:", "ALWAYS EXCITATORY (to thalamus/brainstem) despite inhibitory Purkinje input"),
    ("MCP = largest peduncle:", "Contains ONLY afferents (pontocerebellar)"),
    ("Climbing fibers:", "1 per Purkinje · from inferior olive · motor learning · complex spikes"),
    ("Deep nuclei (lat→med):", "Don't  Eat  Greasy  Food  (Dentate · Emboliform · Globose · Fastigial)"),
    ("Divisions mnemonic:", "VeStibulo = Vestibular/balance | SPino = Synergy | PonTo = Planning/Timing"),
    ("Cerebellar vs Cortical:", "Cerebellar signs IPSILATERAL · Cortical signs CONTRALATERAL"),
]
mn_content = ""
for k, v in pearls:
    mn_content += f"<b>• {k}</b> {v}<br/>"
mn_para = Paragraph(mn_content, S("mns",
    fontSize=6.8, fontName="Helvetica", leading=10,
    textColor=C_BLACK))
mn_cell = Table([[mn_para]], colWidths=[90*mm])
mn_cell.setStyle(TableStyle([
    ("BACKGROUND", (0,0), (-1,-1), HexColor("#FFFBF0")),
    ("TOPPADDING",   (0,0), (-1,-1), 4),
    ("BOTTOMPADDING",(0,0), (-1,-1), 4),
    ("LEFTPADDING",  (0,0), (-1,-1), 5),
    ("RIGHTPADDING", (0,0), (-1,-1), 5),
    ("BOX",          (0,0), (-1,-1), 0.5, C_AMBER),
]))

# Assemble right column
right_col = Table([
    [loc_header],
    [loc_table],
    [SP(4)],
    [mn_header],
    [mn_cell],
], colWidths=[109*mm])
right_col.setStyle(TableStyle([
    ("TOPPADDING",   (0,0), (-1,-1), 0),
    ("BOTTOMPADDING",(0,0), (-1,-1), 0),
    ("LEFTPADDING",  (0,0), (-1,-1), 0),
    ("RIGHTPADDING", (0,0), (-1,-1), 0),
]))

row4 = Table([
    [signs_header, SP(3), right_col],
    [signs_table,  SP(3), ""],
], colWidths=[107*mm, 4*mm, 117*mm])
row4.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),
    ("SPAN",         (2,0), (2,1)),
]))
story.append(row4)
story.append(SP(5))

# ─── FOOTER ──────────────────────────────────────────────────────────────────
footer_data = [[
    Paragraph(
        "Sources: Costanzo Physiology 7e · Ganong's Review of Medical Physiology 26e · Neuroanatomy through Clinical Cases 3e   |   © Orris Medical Study Aid",
        S("ft", fontSize=5.8, fontName="Helvetica-Oblique",
          textColor=HexColor("#888888"), alignment=TA_CENTER))
]]
footer = Table(footer_data, colWidths=[190*mm])
footer.setStyle(TableStyle([
    ("TOPPADDING",   (0,0), (-1,-1), 3),
    ("BOTTOMPADDING",(0,0), (-1,-1), 3),
    ("LINEABOVE",    (0,0), (-1,0), 0.5, C_MGRAY),
]))
story.append(footer)

# ─── Build PDF ────────────────────────────────────────────────────────────────
doc = SimpleDocTemplate(
    OUTPUT,
    pagesize=A4,
    leftMargin=10*mm, rightMargin=10*mm,
    topMargin=10*mm,  bottomMargin=8*mm,
)
doc.build(story)
print(f"PDF saved to: {OUTPUT}")

Running Command

cd /tmp/workspace/cerebellum-cheatsheet && python generate_cheatsheet.py 2>&1

Running Command

ls -lh /tmp/workspace/cerebellum-cheatsheet/Cerebellum_Cheatsheet.pdf

Running Command

python3 -c " from pypdf import PdfReader r = PdfReader('/tmp/workspace/cerebellum-cheatsheet/Cerebellum_Cheatsheet.pdf') print(f'Pages: {len(r.pages)}') print(f'Page size: {r.pages[0].mediabox}') text = r.pages[0].extract_text() print(f'Text length: {len(text)} chars') print('First 300 chars:', text[:300]) "

1 page, A4 size, all the content is there. The small file size is just because there are no embedded images (pure vector text/table PDF). The cheat sheet is ready.

Your one-page A4 cerebellum cheat sheet is ready:

Cerebellum Cheatsheet

PDF Document · PDF

What's on the sheet (all on one A4 page):

Section	Content
Functional Divisions	Vestibulocerebellum / Spinocerebellum / Pontocerebellum table
Cerebellar Peduncles	Superior / Middle / Inferior with direction + key contents
Cortical Layers	Molecular / Purkinje / Granular with contents and notes
5 Cell Types	Excitatory vs Inhibitory colour-coded table
Afferent Fibers	Climbing vs Mossy fibers side-by-side comparison
Deep Nuclei	Dentate, Emboliform, Globose, Fastigial (lateral → medial)
Clinical Signs	All 8 signs of cerebellar disease
Lesion Localization	Flocculonodular / Vermis / Lateral hemisphere + medulloblastoma
Mnemonics & Pearls	8 high-yield exam pearls including "Don't Eat Greasy Food"

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