{"id":"W4362487914","doi":"10.1117/12.2654309","title":"Analyzing colonoscopy training learning curves using comparative hand tracking assessment","year":2023,"lang":"en","type":"article","venue":"","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Queen's University","funders":"","keywords":"Computer science; Artificial intelligence; Learning curve; Motion (physics); Machine learning","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":[0.00293016,0.0003976185,0.0003378576,0.002521289,0.0001672884,0.00053081,0.0003723341,0.0004427158,0.001743097],"category_scores_gemma":[0.02172135,0.0001213094,0.0003194365,0.0007845549,0.000310969,0.0009563786,0.0006193609,0.0002827324,0.0004561452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002632817,"about_ca_system_score_gemma":0.0002201829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009216351,"about_ca_topic_score_gemma":0.0009958195,"domain_scores_codex":[0.9987196,0.0003241611,0.0001418328,0.0002825381,0.0004477585,0.00008423128],"domain_scores_gemma":[0.9837526,0.0105345,0.002041901,0.0009943274,0.002107254,0.0005693764],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002173697,0.0006216228,0.5523967,0.000310238,0.00035469,0.0001914436,0.001659759,0.01840896,0.03008258,0.0004120668,0.0010027,0.3923856],"study_design_scores_gemma":[0.00002449238,0.003681478,0.958538,0.00003719769,0.00004573244,0.0006238124,0.0003858093,0.02582047,0.009750241,0.0002985072,0.0007357699,0.00005856016],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9822963,0.000246763,0.01487969,0.00002152273,0.000009926507,0.00006444396,0.0002280746,0.0002406574,0.002012529],"genre_scores_gemma":[0.9943079,0.00007760488,0.004811931,0.000007148373,0.000005295386,0.00003603899,0.0002954477,0.00002611601,0.0004324842],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00293016,"threshold_uncertainty_score":0.01549637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2661184514539565,"score_gpt":0.4631377903946186,"score_spread":0.1970193389406621,"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."}}