{"id":"W2011832765","doi":"10.1016/j.jsurg.2013.04.006","title":"Application of Stereoscopic Visualization on Surgical Skill Acquisition in Novices","year":2013,"lang":"en","type":"article","venue":"Journal of surgical education","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Schulich School of Medicine and Dentistry; Schulich School of Medicine and Dentistry, Western University; London Health Sciences Centre","keywords":"Knot tying; Visualization; Stereoscopy; Computer science; Task (project management); Human–computer interaction; Artificial intelligence; Computer vision; Medicine; Surgery; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000379627,0.0002019535,0.0001482801,0.0005738985,0.0001327306,0.000138247,0.0001566156,0.0002404344,0.00228248],"category_scores_gemma":[0.002463459,0.00007578651,0.000180097,0.0001451659,0.0001722602,0.0002169024,0.0004423053,0.0002090596,0.0001287699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001382022,"about_ca_system_score_gemma":0.0004574666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002484214,"about_ca_topic_score_gemma":0.004184701,"domain_scores_codex":[0.9998156,0.00005746321,0.0000107541,0.00002572221,0.00004874654,0.0000417316],"domain_scores_gemma":[0.9990344,0.0004993683,0.00005907262,0.00005743102,0.0002249204,0.0001247734],"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":[0.006389231,0.004244693,0.1662374,0.0007470245,0.00008998337,0.001303002,0.003703159,0.004457277,0.2547346,0.0003751929,0.0011463,0.5565721],"study_design_scores_gemma":[0.0001162581,0.01584189,0.9177817,0.0001847837,0.0001121628,0.001536204,0.002158798,0.006660296,0.05304392,0.0004196817,0.002095083,0.00004928405],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976153,0.0001196971,0.0006114393,0.00001619481,0.000007591884,0.00001845707,0.00002736024,0.00001220634,0.001571706],"genre_scores_gemma":[0.9983739,0.0001793257,0.0008243695,0.00001237289,0.000004818038,0.000009057363,0.00002291814,0.000003015172,0.0005703467],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002484214,"threshold_uncertainty_score":0.007635653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01655765285040632,"score_gpt":0.3546284796307384,"score_spread":0.3380708267803321,"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."}}