{"id":"W4387873630","doi":"10.1371/journal.pone.0293178","title":"A webcam-based machine learning approach for three-dimensional range of motion evaluation","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Shoulder Injury and Treatment","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute on Minority Health and Health Disparities","keywords":"Intraclass correlation; Range of motion; Reliability (semiconductor); Motion capture; Goniometer; Computer science; Elbow; Artificial intelligence; Motion (physics); Computer vision; Physical medicine and rehabilitation; Medicine; Physical therapy; Mathematics; Reproducibility; Surgery; Statistics; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001089182,0.0009996712,0.000924786,0.002523303,0.0002379157,0.0009743233,0.000987463,0.00101921,0.007000202],"category_scores_gemma":[0.003489955,0.0002785324,0.0006169872,0.001409985,0.0002736554,0.0007075135,0.001004668,0.0007184939,0.002353209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004507369,"about_ca_system_score_gemma":0.0004673285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001686414,"about_ca_topic_score_gemma":0.002479691,"domain_scores_codex":[0.9987595,0.0002814618,0.00007817805,0.0002966075,0.0005318069,0.00005234864],"domain_scores_gemma":[0.9987179,0.0004761974,0.0001301313,0.0001241213,0.0005057559,0.00004588249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002971213,0.000419434,0.003805328,0.0002664623,0.0001903365,0.0001637066,0.00008368033,0.03322904,0.03078429,0.00129912,0.004052978,0.9254086],"study_design_scores_gemma":[0.00004465954,0.0003059605,0.0117434,0.00006444677,0.00005637226,0.0003211224,0.00006961497,0.9667535,0.01437577,0.002296615,0.003902261,0.00006631073],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0197426,0.000299017,0.9746271,0.00005386642,0.00007105651,0.0002876315,0.0004597307,0.002497367,0.001961578],"genre_scores_gemma":[0.3329555,0.0004087365,0.6593479,0.0001722741,0.00008743173,0.001320767,0.00106322,0.0001987069,0.004445422],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007000202,"threshold_uncertainty_score":0.02341801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1547389960213282,"score_gpt":0.3243025261293809,"score_spread":0.1695635301080527,"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."}}