mkdir -p /tmp/workspace/msk_analysis && ls -la "/tmp/workspace/attachments/6b87f87a-6b12-46b6-a2a6-f128ea468786/"

cd /tmp/workspace/msk_analysis && python3 << 'EOF' import pdfplumber, os files = { "20": "20LM MSK MODULE TEST SOLVED.pdf", "21": "21LM MSK-I MODULE TEST SOLVED.pdf", "22": "22LM MSK-I MODULE TEST SOLVED.pdf", "23": "23LM MSK-I MODULE TEST SOLVED.pdf", "24_final": "24LM MSK-I FINAL PROFF PAPER-II - SOLVED.pdf", "24_module": "24LM MSK-I MODULE TEST SOLVED.pdf", } base = "/tmp/workspace/attachments/6b87f87a-6b12-46b6-a2a6-f128ea468786/" for key, fname in files.items(): path = os.path.join(base, fname) with pdfplumber.open(path) as pdf: print(key, fname, "pages:", len(pdf.pages)) EOF

cd /tmp/workspace/msk_analysis && python3 << 'EOF' import pdfplumber, os files = { "20": "20LM MSK MODULE TEST SOLVED.pdf", "21": "21LM MSK-I MODULE TEST SOLVED.pdf", "22": "22LM MSK-I MODULE TEST SOLVED.pdf", "23": "23LM MSK-I MODULE TEST SOLVED.pdf", "24_final": "24LM MSK-I FINAL PROFF PAPER-II - SOLVED.pdf", "24_module": "24LM MSK-I MODULE TEST SOLVED.pdf", } base = "/tmp/workspace/attachments/6b87f87a-6b12-46b6-a2a6-f128ea468786/" for key, fname in files.items(): path = os.path.join(base, fname) out_text = [] with pdfplumber.open(path) as pdf: for i, page in enumerate(pdf.pages): t = page.extract_text() or "" out_text.append(f"--- PAGE {i+1} ---\n{t}") with open(f"/tmp/workspace/msk_analysis/{key}.txt", "w") as f: f.write("\n".join(out_text)) print(key, "done, chars:", sum(len(x) for x in out_text)) EOF

cd /tmp/workspace/msk_analysis && python3 << 'EOF' import re files = ["20","21","22","23","24_final","24_module"] for key in files: with open(f"{key}.txt") as f: text = f.read() # count questions using regex like Q.1: or Q1: or Q 1: qs = re.findall(r'Q\.?\s?\d+[\.:]', text) print(key, "question markers found:", len(qs)) EOF

cd /tmp/workspace/msk_analysis && python3 << 'EOF' import re, json files = ["20","21","22","23","24_final","24_module"] def split_questions(text): # remove page markers text = re.sub(r'--- PAGE \d+ ---', '', text) # split by question number pattern at start of a question parts = re.split(r'(?=Q\.?\s?\d+[\.:])', text) qs = [p.strip() for p in parts if re.match(r'^Q\.?\s?\d+[\.:]', p.strip())] return qs data = {} for key in files: with open(f"{key}.txt") as f: text = f.read() qs = split_questions(text) data[key] = qs print(key, len(qs)) with open("questions.json","w") as f: json.dump(data, f) EOF

cd /tmp/workspace/msk_analysis && python3 << 'EOF' import json, re from collections import defaultdict, Counter with open("questions.json") as f: data = json.load(f) topics = { "Shoulder & Brachial Plexus": ["shoulder", "scapula", "clavicle", "rotator cuff", "supraspinatus","infraspinatus","subscapularis","teres minor","teres major","deltoid","axilla","axillary","brachial plexus","glenohumeral","acromion","coracoid","winging of scapula","erb","klumpke","quadrangular space"], "Arm & Elbow": ["humerus","humeral","biceps brachii","triceps","brachialis","elbow","cubital fossa","olecranon","supracondylar"], "Forearm & Wrist": ["radius","ulna","radial","ulnar","forearm","wrist","carpal tunnel","carpal bone","pronator","supinator","colles","smith fracture","interosseous membrane"], "Hand": ["metacarpal","phalan","palm","palmar","dupuytren","snuffbox","thenar","hypothenar","claw hand","ape hand","hand of benediction"], "Upper Limb Nerve Injury": ["radial nerve","ulnar nerve","median nerve","axillary nerve","musculocutaneous","long thoracic","suprascapular","wrist drop","claw hand","winged scapula"], "Hip & Gluteal Region": ["hip joint","gluteal","pelvis","pelvic","acetabulum","femoral head","trendelenburg","piriformis"], "Thigh & Knee": ["femur","femoral shaft","quadriceps","hamstring","knee joint","patella","meniscus","cruciate","adductor canal","popliteal"], "Leg & Ankle": ["tibia","fibula","leg ","ankle","tibial","fibular","peroneal","achilles","compartment syndrome"], "Foot": ["tarsal","metatarsal","plantar","foot ","calcaneus","talus","plantar fasciitis"], "Lower Limb Nerve Injury": ["sciatic nerve","femoral nerve","obturator nerve","tibial nerve","common peroneal","foot drop","saphenous nerve"], "Back & Vertebral Column": ["vertebra","vertebral","spine","spinal cord","intervertebral disc","spinal nerve","cauda equina","lamina","pedicle","scoliosis","kyphosis","lordosis"], "Bone Histology & Physiology": ["osteoblast","osteoclast","osteocyte","ossification","bone remodel","wolff's law","osteoid","haversian","bone matrix","calcification","rickets","osteoporosis","osteomalacia"], "Joint Anatomy/Histology": ["synovial","cartilage","ligament","joint capsule","articular","meniscus","bursa"], "Muscle Histology & Physiology": ["sarcomere","muscle contraction","neuromuscular junction","actin","myosin","muscle fiber","muscle spindle","motor unit","tetany","rigor mortis"], "Embryology": ["limb bud","somite","myotome","sclerotome","dermatome","mesenchyme","embryolog"], "Thorax/Ribs": ["rib ","ribs","sternum","intercostal","thoracic outlet","costal"], "Genetics/Connective Tissue Disorders": ["marfan","osteogenesis imperfecta","ehlers-danlos","achondroplasia","muscular dystrophy"], } def classify(qtext): qlow = qtext.lower() scores = {} for topic, kws in topics.items(): cnt = 0 for kw in kws: cnt += qlow.count(kw.lower()) if cnt > 0: scores[topic] = cnt if not scores: return "Other/Unclassified" return max(scores, key=scores.get) results = {} all_topic_counts = Counter() for key, qs in data.items(): counter = Counter() for q in qs: t = classify(q) counter[t] += 1 results[key] = counter all_topic_counts.update(counter) # print table years = ["20","21","22","23","24_module","24_final"] all_topics = sorted(all_topic_counts.keys(), key=lambda x: -all_topic_counts[x]) print(f"{'Topic':40s} " + " ".join(f"{y:>10s}" for y in years) + f" {'Total':>7s}") for t in all_topics: row = [str(results[y].get(t,0)) for y in years] total = sum(results[y].get(t,0) for y in years) print(f"{t:40s} " + " ".join(f"{v:>10s}" for v in row) + f" {total:>7d}") print() for y in years: print(y, "total classified:", sum(results[y].values()), "raw q count:", len(data[y])) with open("classification_results.json","w") as f: json.dump(results, f, default=lambda x: dict(x)) EOF

cd /tmp/workspace/msk_analysis && python3 << 'EOF' import json, re from collections import Counter with open("questions.json") as f: data = json.load(f) topics = { "Shoulder & Brachial Plexus": ["shoulder", "scapula", "clavicle", "rotator cuff", "supraspinatus","infraspinatus","subscapularis","teres minor","teres major","deltoid","axilla","axillary","brachial plexus","glenohumeral","acromion","coracoid","winging of scapula","erb","klumpke","quadrangular space"], "Arm & Elbow": ["humerus","humeral","biceps brachii","triceps","brachialis","elbow","cubital fossa","olecranon","supracondylar"], "Forearm & Wrist": ["radius","ulna","radial","ulnar","forearm","wrist","carpal tunnel","carpal bone","pronator","supinator","colles","smith fracture","interosseous membrane"], "Hand": ["metacarpal","phalan","palm","palmar","dupuytren","snuffbox","thenar","hypothenar","claw hand","ape hand","hand of benediction"], "Upper Limb Nerve Injury": ["radial nerve","ulnar nerve","median nerve","axillary nerve","musculocutaneous","long thoracic","suprascapular","wrist drop","claw hand","winged scapula"], "Hip & Gluteal Region": ["hip joint","gluteal","pelvis","pelvic","acetabulum","femoral head","trendelenburg","piriformis"], "Thigh & Knee": ["femur","femoral shaft","quadriceps","hamstring","knee joint","patella","meniscus","cruciate","adductor canal","popliteal"], "Leg & Ankle": ["tibia","fibula","leg ","ankle","tibial","fibular","peroneal","achilles","compartment syndrome"], "Foot": ["tarsal","metatarsal","plantar","foot ","calcaneus","talus","plantar fasciitis"], "Lower Limb Nerve Injury": ["sciatic nerve","femoral nerve","obturator nerve","tibial nerve","common peroneal","foot drop","saphenous nerve"], "Back & Vertebral Column": ["vertebra","vertebral","spine","spinal cord","intervertebral disc","spinal nerve","cauda equina","lamina","pedicle","scoliosis","kyphosis","lordosis"], "Bone Histology & Physiology": ["osteoblast","osteoclast","osteocyte","ossification","bone remodel","wolff's law","osteoid","haversian","bone matrix","calcification","rickets","osteoporosis","osteomalacia"], "Joint Anatomy/Histology": ["synovial","cartilage","ligament","joint capsule","articular","meniscus","bursa"], "Muscle Histology & Physiology": ["sarcomere","muscle contraction","neuromuscular junction","actin","myosin","muscle fiber","muscle spindle","motor unit","tetany","rigor mortis"], "Embryology": ["limb bud","somite","myotome","sclerotome","dermatome","mesenchyme","embryolog"], "Thorax/Ribs": ["rib ","ribs","sternum","intercostal","thoracic outlet","costal"], "Genetics/Connective Tissue Disorders": ["marfan","osteogenesis imperfecta","ehlers-danlos","achondroplasia","muscular dystrophy"], } def classify(qtext): qlow = qtext.lower() scores = {} for topic, kws in topics.items(): cnt = 0 for kw in kws: cnt += qlow.count(kw.lower()) if cnt > 0: scores[topic] = cnt if not scores: return "Other/Unclassified" return max(scores, key=scores.get) unclassified = [] for key, qs in data.items(): for q in qs: if classify(q) == "Other/Unclassified": unclassified.append((key, q[:200])) print(len(unclassified)) for k,q in unclassified[:40]: print("====", k) print(q) print() EOF

cd /tmp/workspace/msk_analysis && python3 << 'EOF' import json, re from collections import Counter with open("questions.json") as f: data = json.load(f) topics = { "Shoulder & Brachial Plexus": ["shoulder", "scapula", "clavicle", "rotator cuff", "supraspinatus","infraspinatus","subscapularis","teres minor","teres major","deltoid","axilla","axillary","brachial plexus","glenohumeral","acromion","coracoid","winging of scapula","erb","klumpke","quadrangular space"], "Arm & Elbow": ["humerus","humeral","biceps brachii","triceps","brachialis","elbow","cubital fossa","olecranon","supracondylar"], "Forearm & Wrist": ["radius","ulna","radial","ulnar","forearm","wrist","carpal tunnel","carpal bone","pronator","supinator","colles","smith fracture","interosseous membrane"], "Hand": ["metacarpal","phalan","palm","palmar","dupuytren","snuffbox","thenar","hypothenar","claw hand","ape hand","hand of benediction"], "Upper Limb Nerve Injury": ["radial nerve","ulnar nerve","median nerve","axillary nerve","musculocutaneous","long thoracic","suprascapular","wrist drop"], "Hip & Gluteal Region": ["hip joint","gluteal","pelvis","pelvic","acetabulum","femoral head","trendelenburg","piriformis"], "Thigh & Knee": ["femur","femoral shaft","quadriceps","hamstring","knee joint","patella","meniscus","cruciate","adductor canal","popliteal"], "Leg & Ankle": ["tibia","fibula","leg ","ankle","tibial","fibular","peroneal","achilles","compartment syndrome"], "Foot": ["tarsal","metatarsal","plantar","foot ","calcaneus","talus","plantar fasciitis"], "Lower Limb Nerve Injury": ["sciatic nerve","femoral nerve","obturator nerve","tibial nerve","common peroneal","foot drop","saphenous nerve"], "Back & Vertebral Column": ["vertebra","vertebral","spine","spinal cord","intervertebral disc","spinal nerve","cauda equina","lamina","pedicle","scoliosis","kyphosis","lordosis"], "Bone Histology, Growth & Physiology": ["osteoblast","osteoclast","osteocyte","ossification","bone remodel","wolff's law","osteoid","haversian","bone matrix","calcification","rickets","osteoporosis","osteomalacia","metaphysis","diaphysis","epiphys","periosteum","endosteum","growth plate"], "Joint Anatomy/Histology": ["synovial","cartilage","ligament","joint capsule","articular","bursa"], "Muscle Histology & Physiology": ["sarcomere","muscle contraction","actin","myosin","muscle fiber","muscle spindle","motor unit","tetany","rigor mortis","smooth muscle","cardiac muscle","fusiform cells","striations","calcium for contraction","sarcoplasmic reticulum"], "Neuromuscular Pharmacology (NMJ Blockers/Acetylcholine)": ["neuromuscular blocker","neuromuscular junction","acetylcholine","cholinesterase","succinylcholine","atracurium","rocuronium","vecuronium","pancuronium","mivacurium","nicotinic","motor end plate","laudanosine"], "Gout & Anti-gout Pharmacology": ["gout","uric acid","allopurinol","colchicine","probenecid","febuxostat","sulphinpyrazone","xanthine oxidase","uricosuric"], "Embryology (Limb/Germ Layers/Teratogenesis)": ["limb bud","somite","myotome","sclerotome","dermatome","mesenchyme","embryolog","germ layer","neural crest","neuroectoderm","teratogen"], "Thorax/Ribs": ["rib ","ribs","sternum","intercostal","thoracic outlet","costal"], "Genetics/Connective Tissue Disorders": ["marfan","osteogenesis imperfecta","ehlers-danlos","achondroplasia","muscular dystrophy"], "Skin & Integument Histology": ["epidermis","dermis","stratum","sebaceous","basal cell carcinoma","eccrine","apocrine","keratin","melanocyte"], "Breast Histology & Pathology": ["breast","lobule","acini","acinus","alveoli","lactating","paget","mammary"], "Vitamins, Calcium & Nutrition": ["vitamin d","vitamin c","calcium absorption","scurvy","food fortification","food supplement","nutritional","bleeding of gums"], "Glandular Histology (General)": ["tubular gland","acinar gland","compound gland","simple gland","gland type"], "Ergonomics/Posture (Community Medicine)": ["desk","postural defect","back pain, stress"], } def classify(qtext): qlow = qtext.lower() scores = {} for topic, kws in topics.items(): cnt = 0 for kw in kws: cnt += qlow.count(kw.lower()) if cnt > 0: scores[topic] = cnt if not scores: return "Other/Unclassified" return max(scores, key=scores.get) results = {} all_topic_counts = Counter() for key, qs in data.items(): counter = Counter() for q in qs: t = classify(q) counter[t] += 1 results[key] = counter all_topic_counts.update(counter) years = ["20","21","22","23","24_module","24_final"] all_topics = sorted(all_topic_counts.keys(), key=lambda x: -all_topic_counts[x]) print(f"{'Topic':45s} " + " ".join(f"{y:>10s}" for y in years) + f" {'Total':>7s}") for t in all_topics: row = [str(results[y].get(t,0)) for y in years] total = sum(results[y].get(t,0) for y in years) print(f"{t:45s} " + " ".join(f"{v:>10s}" for v in row) + f" {total:>7d}") print() unclassified = [] for key, qs in data.items(): for q in qs: if classify(q) == "Other/Unclassified": unclassified.append((key, q[:150])) print("Remaining unclassified:", len(unclassified)) for k,q in unclassified: print("====",k, q.replace(chr(10)," ")) EOF

cd /tmp/workspace/msk_analysis && python3 << 'EOF' import json, re from collections import Counter with open("questions.json") as f: data = json.load(f) topics = { "Shoulder & Brachial Plexus": ["shoulder", "scapula", "clavicle", "rotator cuff", "supraspinatus","infraspinatus","subscapularis","teres minor","teres major","deltoid","axilla","axillary","brachial plexus","glenohumeral","acromion","coracoid","winging of scapula","erb","klumpke","quadrangular space"], "Arm & Elbow": ["humerus","humeral","biceps brachii","triceps","brachialis","elbow","cubital fossa","olecranon","supracondylar"], "Forearm & Wrist": ["radius","ulna","radial","ulnar","forearm","wrist","carpal tunnel","carpal bone","pronator","supinator","colles","smith fracture","interosseous membrane"], "Hand": ["metacarpal","phalan","palm","palmar","dupuytren","snuffbox","thenar","hypothenar","claw hand","ape hand","hand of benediction"], "Upper Limb Nerve Injury": ["radial nerve","ulnar nerve","median nerve","axillary nerve","musculocutaneous","long thoracic","suprascapular","wrist drop"], "Hip & Gluteal Region": ["hip joint","gluteal","pelvis","pelvic","acetabulum","femoral head","trendelenburg","piriformis"], "Thigh & Knee": ["femur","femoral shaft","quadriceps","hamstring","knee joint","patella","meniscus","cruciate","adductor canal","popliteal"], "Leg & Ankle": ["tibia","fibula","leg ","ankle","tibial","fibular","peroneal","achilles","compartment syndrome"], "Foot": ["tarsal","metatarsal","plantar","foot ","calcaneus","talus","plantar fasciitis"], "Lower Limb Nerve Injury": ["sciatic nerve","femoral nerve","obturator nerve","tibial nerve","common peroneal","foot drop","saphenous nerve"], "Back & Vertebral Column": ["vertebra","vertebral","spine","spinal cord","intervertebral disc","spinal nerve","cauda equina","lamina","pedicle","scoliosis","kyphosis","lordosis"], "Bone Histology, Growth & Physiology": ["osteoblast","osteoclast","osteocyte","ossification","bone remodel","wolff's law","osteoid","haversian","bone matrix","calcification","rickets","osteoporosis","osteomalacia","metaphysis","diaphysis","epiphys","periosteum","endosteum","growth plate"], "Bone Pathology (Osteomyelitis etc.)": ["osteomyelitis","codman","sequestrum","onion-skin","soap-bubble","sunburst","sunray","bone tumor","ewing"], "Joint Anatomy/Histology": ["synovial","cartilage","ligament","joint capsule","articular","bursa"], "Muscle Histology & Physiology": ["sarcomere","muscle contraction","actin","myosin","muscle fiber","muscle spindle","motor unit","tetany","rigor mortis","smooth muscle","cardiac muscle","fusiform cells","striations","calcium for contraction","sarcoplasmic reticulum","isometric","isotonic","isokinetic","eccentric","concentric","muscle adaptation","mitochondria","capillary density"], "Electrophysiology (Action Potential/Ion Channels)": ["action potential","ion channel","depolarization","sodium influx","voltage-gated","repolarization"], "Neuromuscular Pharmacology (NMJ Blockers/Acetylcholine)": ["neuromuscular blocker","neuromuscular junction","acetylcholine","cholinesterase","succinylcholine","atracurium","rocuronium","vecuronium","pancuronium","mivacurium","nicotinic","motor end plate","laudanosine"], "Gout & Anti-gout Pharmacology": ["gout","uric acid","allopurinol","colchicine","probenecid","febuxostat","sulphinpyrazone","xanthine oxidase","uricosuric"], "Rheumatology Pharmacology (DMARDs)": ["dmard","adalimumab","etanercept","azathioprine","rituximab","abatacept","methotrexate","biologic"], "Embryology (Limb/Germ Layers/Teratogenesis)": ["limb bud","somite","myotome","sclerotome","dermatome","mesenchyme","embryolog","germ layer","neural crest","neuroectoderm","teratogen"], "Thorax/Ribs": ["rib ","ribs","sternum","intercostal","thoracic outlet","costal"], "Cranium/Skull Anatomy": ["skull","occipital bone","temporal bone","mandible","frontal bone","sphenoid bone","cranium"], "Genetics/Connective Tissue Disorders": ["marfan","osteogenesis imperfecta","ehlers-danlos","achondroplasia","muscular dystrophy"], "Connective Tissue Biochemistry (GAGs & Collagen)": ["glycosaminoglycan","hyaluronic acid","chondroitin sulfate","dermatan sulfate","keratan sulfate","heparan sulfate","iduronic acid","collagen","hydroxyproline","proline appears","glycine appears"], "Carbohydrate & Energy Metabolism": ["glycogen","glycogenolysis","glycolysis","mcardle","phosphorylase","krebs cycle","oxidative phosphorylation","beta-oxidation","atp synthesis","energy system","anaerobic","aerobic metabolism","hmp shunt"], "Nucleotide Metabolism (Purine/Pyrimidine)": ["pyrimidine","purine","carbamoyl phosphate","cps-ii","cps ii","de novo synthesis"], "Minerals & Trace Elements": ["zinc","copper","wilson's disease","magnesium","selenium","kayser-fleischer","superoxide dismutase","carbonic anhydrase","manganese"], "Skin & Integument Histology": ["epidermis","dermis","stratum","sebaceous","basal cell carcinoma","eccrine","apocrine","keratin","melanocyte"], "Breast Histology & Pathology": ["breast","lobule","acini","acinus","alveoli","lactating","paget","mammary"], "Vitamins, Calcium & Nutrition": ["vitamin d","vitamin c","calcium absorption","scurvy","food fortification","food supplement","nutritional","bleeding of gums","parathyroid hormone","calcitonin"], "Glandular Histology (General)": ["tubular gland","acinar gland","compound gland","simple gland","gland type"], "Ergonomics/Posture (Community Medicine)": ["desk","postural defect","back pain, stress"], "Research Methodology/Behavioral Science": ["literature review","control group","type a personality","type b personality","behavior theory","research method"], "General Receptor Pharmacology": ["m2 receptor","g-protein-coupled","ligand-gated ion channel","tyrosine kinase receptor"], "Thermoregulation Physiology": ["cold environment","vasoconstriction","vasodilation of blood","pale skin"], } def classify(qtext): qlow = qtext.lower() scores = {} for topic, kws in topics.items(): cnt = 0 for kw in kws: cnt += qlow.count(kw.lower()) if cnt > 0: scores[topic] = cnt if not scores: return "Other/Unclassified" return max(scores, key=scores.get) results = {} all_topic_counts = Counter() qmap = {} for key, qs in data.items(): counter = Counter() qtopics = [] for q in qs: t = classify(q) counter[t] += 1 qtopics.append((q[:80].replace(chr(10)," "), t)) results[key] = counter qmap[key] = qtopics all_topic_counts.update(counter) years = ["20","21","22","23","24_module","24_final"] all_topics = sorted(all_topic_counts.keys(), key=lambda x: -all_topic_counts[x]) print(f"{'Topic':50s} " + " ".join(f"{y:>10s}" for y in years) + f" {'Total':>7s}") for t in all_topics: row = [str(results[y].get(t,0)) for y in years] total = sum(results[y].get(t,0) for y in years) print(f"{t:50s} " + " ".join(f"{v:>10s}" for v in row) + f" {total:>7d}") unclassified = sum(1 for y in years for t,tt in qmap[y] if tt=="Other/Unclassified") print("\nRemaining unclassified total:", unclassified) with open("final_results.json","w") as f: json.dump({y: dict(results[y]) for y in years}, f, indent=2) EOF

cd /tmp/workspace/msk_analysis && python3 << 'EOF' import json from collections import Counter with open("final_results.json") as f: results = json.load(f) groups = { "Upper Limb Anatomy (Shoulder/Arm/Forearm/Hand/Nerves)": ["Shoulder & Brachial Plexus","Arm & Elbow","Forearm & Wrist","Hand","Upper Limb Nerve Injury"], "Lower Limb Anatomy (Hip/Thigh/Leg/Foot/Nerves)": ["Hip & Gluteal Region","Thigh & Knee","Leg & Ankle","Foot","Lower Limb Nerve Injury"], "Muscle Physiology, Histology & NMJ Pharmacology": ["Muscle Histology & Physiology","Electrophysiology (Action Potential/Ion Channels)","Neuromuscular Pharmacology (NMJ Blockers/Acetylcholine)"], "Bone Histology, Growth & Pathology": ["Bone Histology, Growth & Physiology","Bone Pathology (Osteomyelitis etc.)"], "Embryology (Limb Development/Teratogenesis)": ["Embryology (Limb/Germ Layers/Teratogenesis)"], "Gout, Rheumatology & Anti-inflammatory Pharmacology": ["Gout & Anti-gout Pharmacology","Rheumatology Pharmacology (DMARDs)"], "Joint Anatomy & Histology": ["Joint Anatomy/Histology"], "Biochemistry (GAGs/Collagen/Metabolism/Minerals)": ["Connective Tissue Biochemistry (GAGs & Collagen)","Carbohydrate & Energy Metabolism","Nucleotide Metabolism (Purine/Pyrimidine)","Minerals & Trace Elements"], "Skin, Breast & Glandular Histology": ["Skin & Integument Histology","Breast Histology & Pathology","Glandular Histology (General)"], "Vitamins & Calcium/Nutrition": ["Vitamins, Calcium & Nutrition"], "Back & Vertebral Column": ["Back & Vertebral Column"], "Other (Thorax/Skull/Genetics/Behavioral Sci/Misc)": ["Thorax/Ribs","Cranium/Skull Anatomy","Genetics/Connective Tissue Disorders","Research Methodology/Behavioral Science","Ergonomics/Posture (Community Medicine)","General Receptor Pharmacology","Thermoregulation Physiology"], } years = ["20","21","22","23","24_module","24_final"] agg = {g: {y:0 for y in years} for g in groups} for g, subs in groups.items(): for y in years: for s in subs: agg[g][y] += results[y].get(s,0) totals = {g: sum(agg[g].values()) for g in groups} order = sorted(groups.keys(), key=lambda g: -totals[g]) print(f"{'Category':55s} " + " ".join(f"{y:>10s}" for y in years) + f" {'Total':>7s}") for g in order: row = [str(agg[g][y]) for y in years] print(f"{g:55s} " + " ".join(f"{v:>10s}" for v in row) + f" {totals[g]:>7d}") print() print("Year totals:", {y: sum(agg[g][y] for g in groups) for y in years}) EOF

~/msk_analysis

cd /tmp/workspace/msk_analysis && python3 << 'EOF' import json, re with open("questions.json") as f: data = json.load(f) # Rebuild classify function (same as before) to tag each question, then extract Q + correct answer + explanation snippet topics = { "Shoulder & Brachial Plexus": ["shoulder", "scapula", "clavicle", "rotator cuff", "supraspinatus","infraspinatus","subscapularis","teres minor","teres major","deltoid","axilla","axillary","brachial plexus","glenohumeral","acromion","coracoid","erb","klumpke","quadrangular space"], "Arm & Elbow": ["humerus","humeral","biceps brachii","triceps","brachialis","elbow","cubital fossa","olecranon","supracondylar"], "Forearm & Wrist": ["radius","ulna","radial","ulnar","forearm","wrist","carpal tunnel","carpal bone","pronator","supinator","colles","smith fracture","interosseous membrane"], "Hand": ["metacarpal","phalan","palm","palmar","dupuytren","snuffbox","thenar","hypothenar","claw hand","ape hand"], "Hip & Gluteal Region": ["hip joint","gluteal","pelvis","pelvic","acetabulum","femoral head","trendelenburg","piriformis"], "Thigh & Knee": ["femur","femoral shaft","quadriceps","hamstring","knee joint","patella","meniscus","cruciate","adductor canal","popliteal"], "Leg & Ankle": ["tibia","fibula","leg ","ankle","tibial","fibular","peroneal","achilles","compartment syndrome"], "Foot": ["tarsal","metatarsal","plantar","foot ","calcaneus","talus"], "Lower Limb Nerve Injury": ["sciatic nerve","femoral nerve","obturator nerve","tibial nerve","common peroneal","foot drop"], "Muscle Physiology & Histology": ["sarcomere","muscle contraction","actin","myosin","muscle fiber","muscle spindle","motor unit","tetany","rigor mortis","smooth muscle","cardiac muscle","isometric","isotonic","sarcoplasmic reticulum"], "NMJ Pharmacology": ["neuromuscular blocker","acetylcholine","cholinesterase","succinylcholine","atracurium","rocuronium","vecuronium","pancuronium","mivacurium","motor end plate"], "Embryology": ["limb bud","somite","myotome","sclerotome","dermatome","mesenchyme","germ layer","neural crest","neuroectoderm","teratogen"], "Bone Histology & Growth": ["osteoblast","osteoclast","osteocyte","ossification","wolff's law","haversian","rickets","osteoporosis","metaphysis","diaphysis","epiphys","periosteum"], "Joint Histology": ["synovial","cartilage","ligament","joint capsule","articular","bursa"], "Gout Pharmacology": ["gout","uric acid","allopurinol","colchicine","probenecid","febuxostat","xanthine oxidase"], "Biochemistry (GAG/Collagen/Metabolism/Minerals)": ["glycosaminoglycan","hyaluronic acid","chondroitin","dermatan","keratan","heparan","iduronic","collagen","hydroxyproline","glycogen","mcardle","zinc","copper","wilson's disease","magnesium","pyrimidine","purine"], "Vitamins & Calcium": ["vitamin d","vitamin c","calcium absorption","scurvy","parathyroid hormone","calcitonin"], "Skin & Breast Histology": ["epidermis","dermis","stratum","sebaceous","basal cell carcinoma","breast","lobule","acini","mammary"], "Back & Vertebral Column": ["vertebra","vertebral","spine","spinal cord","intervertebral disc","scoliosis"], } def classify(qtext): qlow = qtext.lower() scores = {} for topic, kws in topics.items(): cnt = sum(qlow.count(kw.lower()) for kw in kws) if cnt>0: scores[topic]=cnt return max(scores, key=scores.get) if scores else "Other" by_topic = {} for year, qs in data.items(): for q in qs: t = classify(q) by_topic.setdefault(t, []).append((year, q)) for t, items in by_topic.items(): print(t, len(items)) EOF

cd /tmp/workspace/msk_analysis && python3 << 'EOF' import json, re with open("questions.json") as f: data = json.load(f) topics = { "Shoulder & Brachial Plexus": ["shoulder", "scapula", "clavicle", "rotator cuff", "supraspinatus","infraspinatus","subscapularis","teres minor","teres major","deltoid","axilla","axillary","brachial plexus","glenohumeral","acromion","coracoid","erb","klumpke","quadrangular space"], "Arm & Elbow": ["humerus","humeral","biceps brachii","triceps","brachialis","elbow","cubital fossa","olecranon","supracondylar"], "Forearm & Wrist": ["radius","ulna","radial","ulnar","forearm","wrist","carpal tunnel","carpal bone","pronator","supinator","colles","smith fracture","interosseous membrane"], "Hand": ["metacarpal","phalan","palm","palmar","dupuytren","snuffbox","thenar","hypothenar","claw hand","ape hand"], "Hip & Gluteal Region": ["hip joint","gluteal","pelvis","pelvic","acetabulum","femoral head","trendelenburg","piriformis"], "Thigh & Knee": ["femur","femoral shaft","quadriceps","hamstring","knee joint","patella","meniscus","cruciate","adductor canal","popliteal"], "Leg & Ankle": ["tibia","fibula","leg ","ankle","tibial","fibular","peroneal","achilles","compartment syndrome"], "Foot": ["tarsal","metatarsal","plantar","foot ","calcaneus","talus"], "Lower Limb Nerve Injury": ["sciatic nerve","femoral nerve","obturator nerve","tibial nerve","common peroneal","foot drop"], "Muscle Physiology & Histology": ["sarcomere","muscle contraction","actin","myosin","muscle fiber","muscle spindle","motor unit","tetany","rigor mortis","smooth muscle","cardiac muscle","isometric","isotonic","sarcoplasmic reticulum"], "NMJ Pharmacology": ["neuromuscular blocker","acetylcholine","cholinesterase","succinylcholine","atracurium","rocuronium","vecuronium","pancuronium","mivacurium","motor end plate"], "Embryology": ["limb bud","somite","myotome","sclerotome","dermatome","mesenchyme","germ layer","neural crest","neuroectoderm","teratogen"], "Bone Histology & Growth": ["osteoblast","osteoclast","osteocyte","ossification","wolff's law","haversian","rickets","osteoporosis","metaphysis","diaphysis","epiphys","periosteum"], "Joint Histology": ["synovial","cartilage","ligament","joint capsule","articular","bursa"], "Gout Pharmacology": ["gout","uric acid","allopurinol","colchicine","probenecid","febuxostat","xanthine oxidase"], "Biochemistry (GAG/Collagen/Metabolism/Minerals)": ["glycosaminoglycan","hyaluronic acid","chondroitin","dermatan","keratan","heparan","iduronic","collagen","hydroxyproline","glycogen","mcardle","zinc","copper","wilson's disease","magnesium","pyrimidine","purine"], "Vitamins & Calcium": ["vitamin d","vitamin c","calcium absorption","scurvy","parathyroid hormone","calcitonin"], "Skin & Breast Histology": ["epidermis","dermis","stratum","sebaceous","basal cell carcinoma","breast","lobule","acini","mammary"], "Back & Vertebral Column": ["vertebra","vertebral","spine","spinal cord","intervertebral disc","scoliosis"], } def classify(qtext): qlow = qtext.lower() scores = {} for topic, kws in topics.items(): cnt = sum(qlow.count(kw.lower()) for kw in kws) if cnt>0: scores[topic]=cnt return max(scores, key=scores.get) if scores else "Other" def parse_qa(qtext): m = re.search(r'Correct Answer:\s*(.+)', qtext) ans = m.group(1).split("Explanation")[0].strip() if m else "" stem = qtext.split("Correct Answer")[0].strip() stem = re.sub(r'\s+', ' ', stem) exp_m = re.search(r'Explanation:\s*(.+)', qtext, re.DOTALL) exp = re.sub(r'\s+', ' ', exp_m.group(1)).strip()[:220] if exp_m else "" return stem[:300], ans, exp by_topic = {} for year, qs in data.items(): for q in qs: t = classify(q) stem, ans, exp = parse_qa(q) by_topic.setdefault(t, []).append((year, stem, ans, exp)) # dedupe by answer text within topic to get unique facts, keep first occurrence out = {} for t, items in by_topic.items(): seen = set() uniq = [] for year, stem, ans, exp in items: key = ans.lower()[:60] if key in seen: continue seen.add(key) uniq.append((year, stem, ans, exp)) out[t] = uniq for t, items in out.items(): print(f"\n=== {t} ({len(items)} unique / {len(by_topic[t])} total) ===") with open("unique_facts.json","w") as f: json.dump(out, f) EOF

cd /tmp/workspace/msk_analysis && python3 << 'EOF' import json with open("unique_facts.json") as f: out = json.load(f) order = ["Shoulder & Brachial Plexus","Arm & Elbow","Forearm & Wrist","Hand", "Muscle Physiology & Histology","NMJ Pharmacology", "Hip & Gluteal Region","Thigh & Knee","Leg & Ankle","Foot","Lower Limb Nerve Injury", "Embryology","Bone Histology & Growth","Joint Histology","Gout Pharmacology", "Biochemistry (GAG/Collagen/Metabolism/Minerals)","Vitamins & Calcium", "Skin & Breast Histology","Back & Vertebral Column","Other"] for t in order: items = out.get(t, []) print(f"\n########## {t} ##########") for year, stem, ans, exp in items: print(f"[{year}] Q: {stem[:130]} | ANS: {ans}") EOF

mkdir -p /tmp/workspace/msk_analysis/output && cat > /tmp/workspace/msk_analysis/build_pdf.py << 'PYEOF' from reportlab.lib.pagesizes import A4 from reportlab.lib.units import cm from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle from reportlab.lib.enums import TA_LEFT from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, ListFlowable, ListItem, PageBreak from reportlab.lib import colors styles = getSampleStyleSheet() h1 = ParagraphStyle('h1', parent=styles['Heading1'], fontSize=16, textColor=colors.HexColor("#1a4d8f"), spaceAfter=10) h2 = ParagraphStyle('h2', parent=styles['Heading2'], fontSize=13, textColor=colors.HexColor("#0f6e3f"), spaceBefore=12, spaceAfter=6) h3 = ParagraphStyle('h3', parent=styles['Heading3'], fontSize=11, textColor=colors.HexColor("#8a1f1f"), spaceBefore=8, spaceAfter=4) body = ParagraphStyle('body', parent=styles['Normal'], fontSize=9.5, leading=13) sched = ParagraphStyle('sched', parent=styles['Normal'], fontSize=10, leading=14, textColor=colors.HexColor("#1a4d8f")) doc = SimpleDocTemplate("/tmp/workspace/msk_analysis/output/MSK_2Day_Revision_Guide.pdf", pagesize=A4, leftMargin=1.6*cm, rightMargin=1.6*cm, topMargin=1.5*cm, bottomMargin=1.5*cm) story = [] def bullets(items): return ListFlowable([ListItem(Paragraph(i, body), bulletColor=colors.HexColor("#1a4d8f")) for i in items], bulletType='bullet', start='•', leftIndent=12) story.append(Paragraph("MSK-I: 2-Day High-Yield Revision Guide", h1)) story.append(Paragraph("Based on analysis of 437 MCQs from 2020-2024 past papers (Module Tests + 2024 Final Prof Paper)", body)) story.append(Spacer(1, 10)) story.append(Paragraph("DAY 1 - High Yield Core (63% of all past MCQs)", h2)) story.append(Paragraph("Morning (3-4 hrs): Upper Limb Anatomy | Afternoon (2-3 hrs): Muscle Physiology & NMJ Pharmacology | Evening (2-3 hrs): Lower Limb Anatomy", sched)) story.append(Paragraph("1. UPPER LIMB ANATOMY (avg 20 MCQs/paper - biggest topic every year)", h3)) story.append(bullets([ "<b>Shoulder abduction 0-15 deg lost, normal after passive start</b> = Supraspinatus tear (classic Q, repeated 2020-2024)", "<b>Surgical neck of humerus fracture</b> injures Axillary nerve + Posterior circumflex humeral artery (quadrangular space)", "<b>Mid-shaft humerus fracture</b> (radial groove) injures Radial nerve + Profunda brachii artery -> wrist drop, weak elbow extension", "<b>Erb's palsy</b> = upper trunk (C5-C6) injury, arm medially rotated/adducted ('waiter's tip'); <b>Klumpke's palsy</b> = lower trunk (C8-T1), claw hand", "<b>Winging of scapula</b> = Long thoracic nerve injury (serratus anterior palsy) - weak pushing/pressing", "<b>Posterior cord injury</b> = Axillary + Radial nerve together", "<b>Anatomical snuffbox floor</b> = Scaphoid bone; <b>Radial artery</b> runs through it and is at risk in snuffbox injury/scaphoid fracture", "<b>Median cubital vein venipuncture gone wrong</b> -> Brachial artery punctured (lies medial to biceps tendon in cubital fossa)", "<b>Carpal tunnel syndrome</b> = Median nerve compressed deep to flexor retinaculum -> thenar wasting, sensory loss lateral 3.5 fingers", "<b>Claw hand (4th/5th finger)</b> = Ulnar nerve injury -> lumbricals 3&4 weak", "<b>Proximal row carpal bones</b> (lateral to medial): Scaphoid, Lunate, Triquetrum, Pisiform - crush injury classic Q", "<b>Axillary artery divided into 3 parts</b> by Pectoralis minor", "<b>Breast lymphatic drainage</b>: upper lateral quadrant -> Anterior (pectoral) axillary nodes first", "<b>Glenoid labrum</b> deepens glenoid fossa, enhances shoulder stability", "<b>Thumb opposition</b> is key for power grip; <b>Dorsal interossei</b> ABduct fingers (DAB), <b>Palmar interossei</b> ADduct (PAD)", "<b>Deltoid</b> muscle = intramuscular injection site in infants is Vastus lateralis (not deltoid)", ])) story.append(Paragraph("2. MUSCLE PHYSIOLOGY, HISTOLOGY & NMJ PHARMACOLOGY (avg 15-20 MCQs/paper, rising trend)", h3)) story.append(bullets([ "<b>Excitation-contraction coupling</b>: AP -> T-tubules (conduct AP inward) -> Ca2+ released from Sarcoplasmic Reticulum via Ryanodine receptors -> Ca2+ binds Troponin C -> tropomyosin shifts -> myosin-actin cross-bridge (power stroke = myosin head tilts, pulls actin toward M-line)", "<b>A-band stays constant</b> in length during contraction; <b>I-band and H-zone shorten</b>", "<b>First energy source</b> in muscle = stored ATP, then creatine phosphate, then glycolysis/aerobic", "<b>Motor unit</b> = one motor neuron + all muscle fibers it innervates", "<b>Refractory period</b> = period cell cannot respond to a new stimulus", "<b>Myosin ATPase</b> hydrolyzes ATP to ADP+Pi (powers cross-bridge cycling); myosin head has 2 sites: actin-binding + ATPase", "<b>Smooth muscle histology</b>: single central nucleus, fusiform/spindle cells, non-striated, involuntary", "<b>Skeletal muscle</b>: multinucleated, peripheral nuclei, striated; connective tissue layers - Epimysium (whole muscle) > Perimysium (fascicle) > Endomysium (fiber)", "<b>Myasthenia gravis</b> = autoantibodies against ACh receptors (NOT enzyme deficiency) -> skeletal muscle only, fatigable weakness", "<b>Malignant hyperthermia</b> - triggered by Succinylcholine/volatile anesthetics, treated with Dantrolene", "<b>Depolarizing NMJ blocker</b> = Succinylcholine (nicotinic agonist); <b>Non-depolarizing</b> = Tubocurarine, Atracurium (breaks to laudanosine->seizures), Vecuronium, Rocuronium, Pancuronium (long-acting), Mivacurium (short-acting)", "<b>Acetylcholine</b> stored in presynaptic vesicles, broken down by Acetylcholinesterase in synaptic cleft", "<b>Endurance training</b> -> increased mitochondria number; <b>Resistance training</b> -> muscle fiber hypertrophy", "<b>Oxytocin</b> = milk ejection/let-down reflex + bonding; <b>Prolactin</b> = milk production", "<b>M2 receptor</b> (heart) and Nicotinic receptor (NMJ) = both cation channels but M2 is GPCR, Nicotinic is ligand-gated ion channel", ])) story.append(Paragraph("3. LOWER LIMB ANATOMY (huge in 2020 & 2024, avg 15-20 MCQs)", h3)) story.append(bullets([ "<b>Trendelenburg gait</b> (pelvis drops to OPPOSITE side on standing leg) = Gluteus medius/minimus weakness = Superior gluteal nerve injury", "<b>IM injection gluteal region</b> given in upper outer/lateral quadrant to avoid sciatic nerve", "<b>Sciatic nerve</b> usually exits BELOW piriformis through greater sciatic foramen; roots L4-S3; Superior gluteal nerve/vessels exit ABOVE piriformis", "<b>Difficulty rising from sitting</b> = Gluteus maximus weak = Inferior gluteal nerve", "<b>Femoral shaft fracture</b> -> damages Profunda femoris (deep femoral) artery", "<b>Popliteal fossa deepest structure</b> = Popliteal artery (VAN from superficial to deep: vein, artery, nerve - deepest is artery)", "<b>Rectus femoris</b> - only quadriceps muscle that both flexes hip AND extends knee", "<b>Popliteus</b> unlocks the knee joint to permit flexion", "<b>ACL</b> prevents anterior displacement of tibia on femur; <b>PCL</b> prevents posterior displacement of tibia (=anterior displacement of femur on tibia)", "<b>Great saphenous vein</b> - used as CABG graft; runs anterior to medial malleolus; saphenous nerve also anterior to medial malleolus", "<b>Common peroneal (fibular) nerve</b> injury = foot drop (loss of dorsiflexion); <b>Deep fibular nerve</b> = sensory loss between great & 2nd toe; <b>Tibial nerve</b> = loss of plantarflexion/toe standing", "<b>Ankle inversion injury</b> (most common) damages Anterior talofibular ligament (lateral ligament)", "<b>Achilles (tendocalcaneus)</b> = strongest tendon of body, controls plantarflexion", "<b>Medial longitudinal arch keystone</b> = Talus (head of talus); <b>Lateral longitudinal arch keystone</b> = Cuboid", "<b>Sole of foot 2nd layer</b> = Lumbricals + Quadratus plantae", ])) story.append(PageBreak()) story.append(Paragraph("DAY 2 - Consolidation + Fast-Growing 2024 Topics", h2)) story.append(Paragraph("Morning (2-3 hrs): Embryology + Bone/Joint Histology | Afternoon (2-3 hrs): Pharmacology + Biochemistry + Vitamins | Evening: Skin/Breast/Misc + Full past-paper mock (re-attempt all 6 papers rapid-fire)", sched)) story.append(Paragraph("4. EMBRYOLOGY (steady 5-6 MCQs every year - easy guaranteed marks)", h3)) story.append(bullets([ "<b>Limb bud / bones & muscles of limb</b> arise from (parietal/somatic) layer of Lateral Plate Mesoderm; <b>limb musculature</b> specifically from Myotomes of somites", "<b>Vertebrae</b> develop from Sclerotome of somite (paraxial mesoderm)", "<b>Iris/eye muscles</b> develop from Neural plate ectoderm (neuroectoderm) - NOT mesoderm (exception to rule)", "<b>Head & neck muscles</b> from Somitomeres + occipital somites (paraxial mesoderm); NOT from endoderm", "<b>Teratogen-sensitive period for limb defects</b> = 4th-8th week (embryonic period, organogenesis)", "<b>Syndactyly</b> (fused fingers) = failure of apoptosis in interdigital mesenchyme/hand plate; normally Apical Ectodermal Ridge signals apoptosis to separate digits", "<b>Amelia</b> (absent limb) traced to failure of lateral plate mesoderm outgrowth", "<b>Upper limb before rotation</b> lies ventrally with thumb side facing medially, then laterally rotates", "<b>Skull vault (calvaria)</b> = membranous ossification; <b>Skull base</b> = endochondral ossification", ])) story.append(Paragraph("5. BONE HISTOLOGY, GROWTH & PATHOLOGY", h3)) story.append(bullets([ "<b>Growth in height/length</b> occurs at Epiphyseal (growth) plate, within Metaphysis region", "<b>Osteocytes</b> sit in lacunae, connected via canaliculi; found in Compact bone (Haversian systems/osteons -> concentric lamellae)", "<b>Osteoporosis</b> = decreased bone density/mass, normal mineralization (vs Osteomalacia = defective mineralization, low Ca, high ALP)", "<b>Osteoporosis Rx</b>: Alendronate = most potent ORAL bisphosphonate; Zoledronate = most potent PARENTERAL bisphosphonate; Denosumab = RANKL inhibitor; Romosozumab = sclerostin inhibitor (used when bisphosphonates fail)", "<b>Calcitonin</b> inhibits osteoclasts (decreases bone resorption); <b>PTH</b> stimulates osteoclastic activity, increases blood Ca2+", "<b>Fracture healing sequence</b>: Hematoma/inflammation -> Soft callus -> Hard callus -> Remodeling", "<b>Osteomyelitis</b>: fever + localized bone pain/tenderness/warmth/swelling; pathognomonic sign = Sequestrum (dead bone fragment)", "<b>Rickets</b> (children) / <b>Osteomalacia</b> (adults) both from Vitamin D deficiency", ])) story.append(Paragraph("6. JOINT ANATOMY & HISTOLOGY", h3)) story.append(bullets([ "<b>Osteoarthritis</b> = mechanical wear/loss of articular cartilage (degenerative, NOT autoimmune)", "<b>Rheumatoid arthritis</b> = autoimmune chronic inflammatory synovitis (systemic)", "<b>Elastic cartilage</b> (epiglottis, ear) stained with Verhoeff's stain; contains elastin fiber bundles (gives flexibility)", "<b>Hyaline cartilage</b> = costal cartilage, articular surfaces; <b>Fibrocartilage</b> = intervertebral disc (annulus fibrosus), menisci - dense collagen, no elastic fibers", "<b>Periosteum outer fibrous layer</b> = attachment for muscles/tendons/ligaments (Sharpey's fibers); inner layer = osteogenic", ])) story.append(Paragraph("7. GOUT, RHEUMATOLOGY & RELATED PHARMACOLOGY (huge spike in 2021 - watch closely)", h3)) story.append(bullets([ "<b>Allopurinol & Febuxostat</b> = Xanthine oxidase inhibitors (decrease URIC ACID SYNTHESIS) - chronic gout prophylaxis", "<b>Probenecid & Sulphinpyrazone</b> = Uricosuric agents (increase renal excretion of uric acid)", "<b>Colchicine</b> = drug of choice for ACUTE gout attack; inhibits leukocyte migration/microtubule (tubulin) polymerization - anti-inflammatory, NOT for chronic uric acid lowering", "<b>HGPRT deficiency</b> -> Lesch-Nyhan syndrome (severe, self-mutilation) or gout in milder form; <b>Xanthine oxidase deficiency</b> -> Xanthinuria", "<b>DMARDs</b>: conventional synthetic = Azathioprine, Methotrexate; biologic = Adalimumab, Etanercept, Rituximab, Abatacept", ])) story.append(Paragraph("8. BIOCHEMISTRY: GAGs, Collagen, Metabolism & Minerals (sharply increasing in 2024)", h3)) story.append(bullets([ "<b>Iduronic acid</b> found in Dermatan sulfate and Heparin/Heparan sulfate (memorize: NOT in hyaluronic acid or keratan sulfate)", "<b>Collagen structure</b>: Glycine at every 3rd position (Gly-X-Y repeat); Prolyl/Lysyl hydroxylase hydroxylates proline/lysine (needs Vitamin C) to stabilize triple helix", "<b>Vitamin C deficiency</b> = Scurvy = defective collagen hydroxylation -> bleeding gums, poor wound healing, bruising", "<b>Collagen secreted by</b> Fibroblasts (also chondroblasts for cartilage, osteoblasts for bone)", "<b>Marfan syndrome</b> = Fibrillin-1 defect; <b>Ehlers-Danlos syndrome</b> = defect in collagen post-translational modification/structure; <b>Osteogenesis imperfecta</b> = Type I collagen defect", "<b>McArdle's disease</b> = Muscle glycogen phosphorylase deficiency (exercise intolerance, no lactate rise); <b>Von Gierke's</b> = Glucose-6-phosphatase deficiency (hepatomegaly, fasting hypoglycemia)", "<b>Glycogenolysis end product</b> in muscle = Glucose-6-phosphate (used locally, no G6Pase in muscle)", "<b>Copper+Zinc containing enzyme</b> = Superoxide dismutase (Cu/Zn-SOD)", "<b>Wilson's disease</b> = Copper accumulation (Kayser-Fleischer rings, liver/CNS disease); treated with Zinc / chelators (penicillamine)", "<b>Zinc deficiency</b> = poor wound healing, loss of taste (hypogeusia)", "<b>Magnesium</b> = drug of choice for pre-eclampsia (decreases neuromuscular irritability); hypercalcemia/hypermagnesemia inhibit renal Mg reabsorption", "<b>Pyrimidine synthesis</b>: Carbamoyl phosphate formed in CYTOPLASM by CPS-II using nitrogen from Glutamine (vs urea cycle CPS-I in mitochondria uses ammonia)", "<b>Purine de novo synthesis</b> does NOT occur in brain/RBCs (they rely on salvage pathway - HGPRT)", ])) story.append(Paragraph("9. VITAMINS, CALCIUM & BONE-MINERAL HORMONES (rising in 2024)", h3)) story.append(bullets([ "<b>Vitamin D pathway</b>: Skin (7-dehydrocholesterol) -> Liver: 25-hydroxycholecalciferol (calcidiol, STORAGE form) -> Kidney: 1,25-dihydroxycholecalciferol (calcitriol, ACTIVE form, has 3 OH groups)", "<b>Calcitriol</b> increases gut calcium absorption via calcium-binding proteins (calbindin)", "<b>PTH</b>: increases bone resorption (Ca release), increases renal Ca reabsorption, increases renal 1-alpha-hydroxylase activity (more calcitriol) -> increases gut Ca absorption indirectly", "<b>Calcitonin</b> secreted by Thyroid parafollicular C-cells; lowers blood calcium by inhibiting osteoclasts", "<b>Low Ca + High Phosphate</b> = Hypoparathyroidism (PTH deficiency)", "<b>Insulin release</b> requires Calcium influx into beta cells", ])) story.append(Paragraph("10. SKIN, BREAST & GLANDULAR HISTOLOGY", h3)) story.append(bullets([ "<b>Basal cell carcinoma</b> arises from Stratum basale (basal layer) of epidermis", "<b>Stratum corneum</b> = outermost, water-resistant/water-loss barrier layer", "<b>Stratum lucidum</b> present only in thick skin (palms & soles)", "<b>Sebaceous glands</b> = Holocrine secretion (entire cell disintegrates into secretion)", "<b>Breast (mammary gland)</b> = Compound tubulo-alveolar (tubulo-acinar) gland; lactating breast shows distended acini/alveoli with milk, thinned septa", ])) story.append(Paragraph("11. QUICK-FIRE MISC (Back, thorax, research methods - low yield but easy 1-2 marks)", h3)) story.append(bullets([ "<b>Pott's disease</b> (spinal TB) caused by Mycobacterium tuberculosis", "<b>Femoral pulse</b> palpated at Mid-inguinal point", "<b>Nicotinic receptor</b> = ligand-gated ion channel; <b>M2 receptor</b> = GPCR (on heart, slows HR)", "<b>Type A personality</b> = competitive, time-pressured, hostile (CVD risk); <b>Control group</b> = baseline for comparing experimental effect", ])) story.append(Spacer(1,10)) story.append(Paragraph("STUDY STRATEGY FOR YOUR 2 DAYS", h2)) story.append(bullets([ "<b>Day 1</b>: Focus only on the 3 biggest topics above (Upper Limb, Muscle Physiology/Pharm, Lower Limb) = ~63% of all historical MCQs. Do NOT move to Day 2 material until these are solid.", "<b>Day 2 morning/afternoon</b>: Cover remaining topics in the order listed (Embryology -> Bone/Joint -> Pharm -> Biochem -> Vitamins) - these are lower per-topic yield individually but together are still ~35% of the paper.", "<b>Day 2 evening</b>: Re-attempt all 6 attached past papers as a timed mock (or at minimum, re-read every question stem + correct answer once) - many questions REPEAT almost verbatim across years (e.g. supraspinatus abduction, gout drugs, Trendelenburg gait, snuffbox artery), so recognizing the pattern is worth more than re-deriving from first principles.", "<b>Pay special attention</b> to 2024 papers - they show clear expansion into biochemistry (GAGs, collagen, minerals) and vitamins/calcium that were minimal in 2020-2022. If your upcoming exam follows the same setters, expect similar cross-disciplinary questions.", ])) doc.build(story) print("PDF created successfully") PYEOF python3 /tmp/workspace/msk_analysis/build_pdf.py

I'll go through all 11 topics one by one (deep explanation each). Given time is tight, which should I start with?