{"id":"W2255254945","doi":"10.1016/j.jsurg.2015.11.013","title":"Can Multiple Object Tracking Predict Laparoscopic Surgical Skills?","year":2016,"lang":"en","type":"article","venue":"Journal of surgical education","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan; University of Regina; Regina Qu'Appelle Health Region","funders":"","keywords":"Tracking (education); Object (grammar); Artificial intelligence; Computer science; Medicine; Computer vision; Medical physics; General surgery; Psychology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005484901,0.0001465596,0.0004272232,0.0001947279,0.00007069402,0.00003077561,0.00008856034,0.0001319281,0.001059564],"category_scores_gemma":[0.0005490245,0.0000846966,0.0002603995,0.000249446,0.00008101173,0.0002072464,0.00001426981,0.0002667191,0.00002533349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001728527,"about_ca_system_score_gemma":0.0005739496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000102703,"about_ca_topic_score_gemma":0.000003051272,"domain_scores_codex":[0.9982527,0.0001064816,0.0006904936,0.0001722026,0.000527237,0.0002508776],"domain_scores_gemma":[0.9977933,0.0008889168,0.0003272611,0.0001464374,0.0004046796,0.0004393808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001407026,0.002282474,0.4541813,0.00006453037,0.0001724904,0.0005861676,0.0006178548,0.00003989216,0.0006552382,0.0009080502,0.001073135,0.5380118],"study_design_scores_gemma":[0.03166994,0.0009762888,0.3831362,0.001795129,0.0002350422,0.004468975,0.0005390647,0.0001513525,0.003226058,0.0007027232,0.5727158,0.0003833716],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9868703,0.0003347889,0.0000452645,0.00576639,0.0009732715,0.0001425315,0.000003370296,0.00003230014,0.005831781],"genre_scores_gemma":[0.9966669,0.0002763968,0.0002175322,0.0001628431,0.001444387,0.000004048538,0.00000669244,0.00001890779,0.001202267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5716427,"threshold_uncertainty_score":0.9998536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02266907952323211,"score_gpt":0.3257883305844027,"score_spread":0.3031192510611706,"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."}}