{"id":"W2911731865","doi":"10.1109/tbme.2018.2837620","title":"Proposal and Validation of a Knee Measurement System for Patients With Osteoarthritis","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Total Knee Arthroplasty Outcomes","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research; Arthritis Society","keywords":"Imaging phantom; Wearable computer; Computer science; Instrumentation (computer programming); Motion capture; Knee Joint; Degrees of freedom (physics and chemistry); Gold standard (test); Osteoarthritis; Calibration; Computer vision; Artificial intelligence; Remote patient monitoring; Valgus; Simulation; Medicine; Motion (physics); Mathematics; Orthodontics; Surgery; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001084517,0.0001155845,0.000203215,0.0001597209,0.00004826644,0.000006531514,0.00002300544,0.00006395843,0.00001052919],"category_scores_gemma":[0.00001874506,0.00009113173,0.00004337936,0.0001541994,0.0001052205,0.00005753231,6.933691e-7,0.00007625505,0.00000420408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007859278,"about_ca_system_score_gemma":0.00005140056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004983854,"about_ca_topic_score_gemma":0.000002925615,"domain_scores_codex":[0.9990377,0.000006849779,0.000211684,0.000162046,0.0004237262,0.0001579794],"domain_scores_gemma":[0.9994883,0.00003593816,0.00003578583,0.0001174918,0.0001851763,0.0001373305],"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":[0.00966987,0.005750958,0.007182066,0.01198169,0.002464287,0.00004507443,0.003219439,0.002555897,0.2553962,0.0006751254,0.0002719328,0.7007874],"study_design_scores_gemma":[0.06092519,0.05966333,0.03395949,0.007775739,0.00127885,0.0002662528,0.0004800697,0.02767294,0.8014983,0.00001010572,0.005147331,0.001322427],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4866374,0.00001315869,0.5120343,0.0001123406,0.0004012013,0.0006527099,0.00003701779,0.00009639527,0.0000154623],"genre_scores_gemma":[0.9930007,0.000001384832,0.006820153,0.000008831288,0.00005980503,0.00006697146,0.000005160475,0.0000234661,0.00001354313],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.699465,"threshold_uncertainty_score":0.3716244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008562906649593074,"score_gpt":0.2089330503584963,"score_spread":0.2003701437089032,"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."}}