{"id":"W3116540513","doi":"10.1016/j.medengphy.2020.12.007","title":"Machine learning and wearable sensors at preoperative assessments: Functional recovery prediction to set realistic expectations for knee replacements","year":2020,"lang":"en","type":"article","venue":"Medical Engineering & Physics","topic":"Total Knee Arthroplasty Outcomes","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Arthritis Society; Canadian Institutes of Health Research; Government of Ontario","keywords":"Wearable computer; Set (abstract data type); Computer science; Machine learning; Wearable technology; Artificial intelligence; Human–computer interaction; Biomedical engineering; Physical medicine and rehabilitation; Simulation; Medicine; Embedded system","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":[0.002098218,0.0005074157,0.0005426335,0.0005240206,0.0001199184,0.0005737514,0.000272937,0.0005291142,0.0004115608],"category_scores_gemma":[0.008157626,0.0001416176,0.0004217294,0.0003017785,0.0001826971,0.0004543655,0.0002731088,0.0005553471,0.0002484098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002102216,"about_ca_system_score_gemma":0.0003454284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001267229,"about_ca_topic_score_gemma":0.00157609,"domain_scores_codex":[0.9991323,0.00045444,0.0000732254,0.0001132977,0.0001555896,0.00007113045],"domain_scores_gemma":[0.9965155,0.00204903,0.0007042331,0.0001405928,0.0004633699,0.0001272271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002590829,0.001140636,0.6461785,0.0002144855,0.0002375948,0.0001750849,0.0004643461,0.03688014,0.009291123,0.0002045174,0.001344929,0.3012778],"study_design_scores_gemma":[0.00006789821,0.002465305,0.3848144,0.000120854,0.0001138952,0.0004552265,0.0006007239,0.6032765,0.005936712,0.001384315,0.0006958268,0.00006841117],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9800252,0.0004551747,0.0185975,0.0001960447,0.00002888578,0.00003150539,0.0001553264,0.0001058179,0.0004045228],"genre_scores_gemma":[0.9946137,0.00007090274,0.005021791,0.00002863875,0.00001441859,0.00001637474,0.0001366678,0.000003187115,0.00009412421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002098218,"threshold_uncertainty_score":0.0110966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02112606929461351,"score_gpt":0.2783862009466105,"score_spread":0.257260131651997,"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."}}