{"id":"W2789794229","doi":"10.1093/jcag/gwy008.020","title":"A19 DEVELOPING A COMPETENCY-BASED PERFORMANCE METRIC OF COLONOSCOPY SKILLS ACQUISITION USING MOTION ANALYSIS - STEP 1: LOW-FIDELITY BENCHTOP MODEL","year":2018,"lang":"en","type":"article","venue":"Journal of the Canadian Association of Gastroenterology","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; The Wilson Centre; Hospital for Sick Children; Queen's University","funders":"","keywords":"Colonoscopy; Competence (human resources); Fidelity; Dreyfus model of skill acquisition; Curriculum; Medicine; Computer science; Simulation; Medical physics; Physical therapy; Psychology; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002415491,0.0005299551,0.0003965873,0.0008066484,0.0002497466,0.0008506232,0.0005713086,0.0006027311,0.001855047],"category_scores_gemma":[0.008198705,0.0001796715,0.0006902102,0.0004395301,0.0002529976,0.0006044089,0.0007681798,0.0004881712,0.0004692184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005759639,"about_ca_system_score_gemma":0.0008442726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004040504,"about_ca_topic_score_gemma":0.003991556,"domain_scores_codex":[0.9985696,0.0003922374,0.0001700932,0.0001556374,0.000611469,0.0001008437],"domain_scores_gemma":[0.9958847,0.001484805,0.000722033,0.0002944892,0.001355428,0.0002585154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002596257,0.0036958,0.3731472,0.001387071,0.0004644528,0.0002768892,0.001105397,0.06795532,0.1645635,0.002228807,0.003431494,0.3791478],"study_design_scores_gemma":[0.0001703197,0.01772866,0.6286292,0.0002712151,0.0002167919,0.0007458106,0.0009377929,0.2617288,0.08218917,0.0009313143,0.006223904,0.0002269682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8683888,0.0002508564,0.1244087,0.0001112866,0.00004691696,0.00108969,0.000981238,0.0003709517,0.004351565],"genre_scores_gemma":[0.9192587,0.0001286058,0.07751109,0.00004550625,0.000006890955,0.0008325081,0.001003215,0.0000179713,0.001195459],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004040504,"threshold_uncertainty_score":0.01277453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01629508382095293,"score_gpt":0.2755148583120212,"score_spread":0.2592197744910683,"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."}}