{"id":"W4386483511","doi":"10.21203/rs.3.rs-3320608/v1","title":"Joint Angle Estimation during Shoulder Abduction Exercise Using Contactless Technology","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"","keywords":"Motion capture; Computer science; Artificial intelligence; Calibration; Joint (building); Computer vision; Ground truth; Noise (video); Flexibility (engineering); Motion (physics); Mathematics; Statistics; Engineering; Image (mathematics)","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":[0.0002398393,0.0005249027,0.0003645407,0.0009397952,0.000118039,0.0004309864,0.0003809527,0.000540912,0.001957442],"category_scores_gemma":[0.001372878,0.0001740098,0.0002477829,0.0006012942,0.0001372167,0.0004113143,0.0005375659,0.0001782446,0.0007517386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001228484,"about_ca_system_score_gemma":0.0001789834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009435293,"about_ca_topic_score_gemma":0.001934827,"domain_scores_codex":[0.9996207,0.00005827888,0.00002237963,0.0001205165,0.0001534229,0.00002469847],"domain_scores_gemma":[0.9995868,0.0001002471,0.00005911086,0.00003840703,0.0001907076,0.0000248756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001632982,0.0003714691,0.06850314,0.00123656,0.0002414191,0.0005652431,0.0006630396,0.01426294,0.2983766,0.0005142944,0.00315569,0.6104766],"study_design_scores_gemma":[0.0001714148,0.002043218,0.5002735,0.0003161198,0.0003239832,0.003293978,0.0007275698,0.3171373,0.1643708,0.001329434,0.009815882,0.0001967377],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7735283,0.0009664424,0.2166154,0.0001062899,0.0001325233,0.0001631449,0.001729885,0.001264511,0.005493405],"genre_scores_gemma":[0.9704859,0.0002387787,0.02726142,0.00007516831,0.00003054998,0.00008092442,0.0005109496,0.00002550133,0.001290799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001957442,"threshold_uncertainty_score":0.006548285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2369142993054175,"score_gpt":0.5038869166218518,"score_spread":0.2669726173164343,"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."}}