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