{"id":"W4280567810","doi":"10.1371/journal.pcbi.1009500","title":"Predicting knee adduction moment response to gait retraining with minimal clinical data","year":2022,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Osteoarthritis Treatment and Mechanisms","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Science Foundation","keywords":"Gait; Physical medicine and rehabilitation; Osteoarthritis; Retraining; Medicine; Physical therapy; Gait analysis; Test set; Computer science; Artificial intelligence; Machine learning; Pathology","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":[],"consensus_categories":[],"category_scores_codex":[0.0006442805,0.0001090676,0.0002335961,0.0001282168,0.0001900647,0.000007552614,0.0001105238,0.00004696141,0.0003012914],"category_scores_gemma":[0.0001939981,0.00009702305,0.00003294861,0.0001683688,0.00005066867,0.00004545356,0.0002565396,0.0002132445,0.00002962471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008478075,"about_ca_system_score_gemma":0.0002090087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002976216,"about_ca_topic_score_gemma":0.000002556576,"domain_scores_codex":[0.9984927,0.0002890468,0.0003117186,0.0004671033,0.0002398613,0.000199608],"domain_scores_gemma":[0.9990296,0.0004330814,0.0000911837,0.0002466526,0.00007445231,0.0001250602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.1080764,0.005343927,0.584357,0.0000899595,0.001936384,0.00115582,0.004082696,0.006628698,0.04704567,0.005328675,0.008180102,0.2277747],"study_design_scores_gemma":[0.06001558,0.3086257,0.33955,0.0005677716,0.001607663,0.004788903,0.007722049,0.1446891,0.001394255,0.003891569,0.125176,0.001971477],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946968,0.00006191986,0.001087053,0.0027927,0.0002932289,0.0005017119,0.0002489167,0.0000888809,0.0002287804],"genre_scores_gemma":[0.9701452,0.000001142762,0.02619805,0.0009000074,0.0002817229,0.00009432143,0.002148497,0.00001725511,0.000213753],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3032818,"threshold_uncertainty_score":0.3956485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08998604220468466,"score_gpt":0.3483893077379286,"score_spread":0.258403265533244,"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."}}