{"id":"W3080817534","doi":"10.1016/j.arth.2020.08.034","title":"Machine Learning Predicts the Fall Risk of Total Hip Arthroplasty Patients Based on Wearable Sensor Instrumented Performance Tests","year":2020,"lang":"en","type":"article","venue":"The Journal of Arthroplasty","topic":"Total Knee Arthroplasty Outcomes","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lawson Health Research Institute; Western University","funders":"Arthritis Society","keywords":"Receiver operating characteristic; Wearable computer; Machine learning; Linear discriminant analysis; Artificial intelligence; Support vector machine; Classifier (UML); Cross-validation; Medicine; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004855107,0.000651971,0.0005242607,0.001053895,0.0002145371,0.0007958011,0.0003644247,0.0006744138,0.00139071],"category_scores_gemma":[0.004210755,0.0001795644,0.0007135056,0.000746072,0.0001807773,0.0004717703,0.0003926423,0.0006276626,0.0004498743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001975889,"about_ca_system_score_gemma":0.0002589977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00314793,"about_ca_topic_score_gemma":0.004040782,"domain_scores_codex":[0.9996452,0.00007663127,0.0000536716,0.00007932164,0.00008127336,0.00006391653],"domain_scores_gemma":[0.9982705,0.000600929,0.0006120121,0.00009951298,0.0002473062,0.0001697228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000219818,0.0001443654,0.9909735,0.00001253833,0.0001181373,0.00004639804,0.00001981228,0.0009411338,0.000233289,0.00003221831,0.0002558301,0.007002816],"study_design_scores_gemma":[0.00001516655,0.0003454565,0.9577835,0.00002207564,0.0001058406,0.0003393827,0.0001539134,0.04029254,0.0003032379,0.0004058046,0.000214827,0.0000181063],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971223,0.0002333613,0.001095049,0.0001205518,0.00002864668,0.000007939882,0.0008357238,0.00002407225,0.0005323163],"genre_scores_gemma":[0.9986334,0.00009956017,0.0003333021,0.00001997268,0.00002031712,0.00000656425,0.0006755518,0.000002049576,0.0002092412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00314793,"threshold_uncertainty_score":0.006259203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01081924087959362,"score_gpt":0.2142018164840401,"score_spread":0.2033825756044464,"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."}}