{"id":"W2885545611","doi":"10.1007/s00268-018-4731-6","title":"The Use of Three‐Dimensional Printing Model in the Training of Choledochoscopy Techniques","year":2018,"lang":"en","type":"article","venue":"World Journal of Surgery","topic":"Anatomy and Medical Technology","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal","funders":"Beijing Municipal Administration of Hospitals; Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support","keywords":"3d printed; Medicine; Biliary tract; 3d model; Surgery; Medical physics; Radiology; Artificial intelligence; Computer science; Biomedical engineering","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":[0.001653214,0.0004314586,0.0002834258,0.0006496747,0.0001776067,0.0006304219,0.0004085472,0.0007368308,0.001502466],"category_scores_gemma":[0.006065532,0.0002686055,0.0005987221,0.0003120542,0.0005109342,0.00056735,0.0006331481,0.0002984073,0.0005129124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002610703,"about_ca_system_score_gemma":0.0003991191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005908996,"about_ca_topic_score_gemma":0.0004931928,"domain_scores_codex":[0.9984769,0.0005944021,0.00008032954,0.0001457367,0.0005969668,0.0001056129],"domain_scores_gemma":[0.9973549,0.001291005,0.0003366165,0.0005297851,0.0003282786,0.0001595115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.006276601,0.002597301,0.1572445,0.0009607138,0.0001826958,0.002786961,0.002160666,0.02015542,0.3219291,0.0003784224,0.0009551967,0.4843724],"study_design_scores_gemma":[0.0005760131,0.0719267,0.3956756,0.0004184005,0.001027027,0.03392938,0.00159612,0.1112421,0.3619013,0.0007785266,0.02047394,0.0004548394],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990099,0.0006805541,0.008003031,0.00007801686,0.00003502032,0.00003939047,0.00003786093,0.00007649336,0.0009505895],"genre_scores_gemma":[0.9894025,0.0003490985,0.009754323,0.00004374163,0.00001535119,0.00002578624,0.0000671273,0.00001549901,0.0003264699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001653214,"threshold_uncertainty_score":0.008743107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09010639591359684,"score_gpt":0.2728386142976096,"score_spread":0.1827322183840128,"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."}}