{"id":"W4281563464","doi":"10.3390/biomedicines10061247","title":"A Machine Learning Model to Predict Knee Osteoarthritis Cartilage Volume Changes over Time Using Baseline Bone Curvature","year":2022,"lang":"en","type":"article","venue":"Biomedicines","topic":"Osteoarthritis Treatment and Mechanisms","field":"Medicine","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"PerkinElmer Biosignal; Université de Montréal","funders":"GlaxoSmithKline; Novartis Pharmaceuticals Corporation; Pfizer; National Institutes of Health; U.S. Department of Health and Human Services; Foundation for the National Institutes of Health","keywords":"Osteoarthritis; Cartilage; Medicine; Magnetic resonance imaging; Body mass index; Knee Joint; Surgery; Internal medicine; Pathology; Anatomy; Radiology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004738927,0.0003885442,0.0006662452,0.000556161,0.0004213752,0.00002226612,0.0001151621,0.000119748,0.001839299],"category_scores_gemma":[0.0001239632,0.0003548461,0.0001390022,0.000752607,0.00007560071,0.00009086529,0.0002619078,0.0004098987,0.00007227452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001904737,"about_ca_system_score_gemma":0.0001464184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007994436,"about_ca_topic_score_gemma":0.00004689797,"domain_scores_codex":[0.9974955,0.0001173164,0.0003747937,0.000593255,0.0008163377,0.0006028538],"domain_scores_gemma":[0.9988807,0.00004171276,0.0001320445,0.0004008092,0.0001091528,0.0004355414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001371012,0.0004505849,0.006609629,0.000146464,0.00009403098,0.001448793,0.001577457,0.0008277719,0.8942496,0.00003885083,0.009059616,0.08412619],"study_design_scores_gemma":[0.03247497,0.04851588,0.0006924061,0.001404773,0.002018929,0.003269131,0.0009567153,0.3165052,0.01348143,0.0001510837,0.5786492,0.001880215],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9780899,0.01183356,0.000594909,0.004844969,0.000869243,0.001818605,0.0007228319,0.0005360986,0.0006899264],"genre_scores_gemma":[0.9545755,0.00006863449,0.003724508,0.003169865,0.00130256,0.0003081072,0.001487507,0.0001601382,0.0352032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8807682,"threshold_uncertainty_score":0.9998903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01566323861920654,"score_gpt":0.247712669325044,"score_spread":0.2320494307058375,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}