{"id":"W4391218192","doi":"10.1007/s00384-024-04593-z","title":"Development of an image-based Random Forest classifier for prediction of surgery duration of laparoscopic sigmoid resections","year":2024,"lang":"en","type":"article","venue":"International Journal of Colorectal Disease","topic":"Diverticular Disease and Complications","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Technische Universität München","keywords":"Medicine; Sigmoid function; Random forest; Hepatology; Laparoscopic surgery; General surgery; Classifier (UML); Surgery; Laparoscopy; Artificial intelligence; Radiology; Computer science; Artificial neural network","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":[],"consensus_categories":[],"category_scores_codex":[0.0002582998,0.00007794106,0.0002286671,0.00039042,0.00002914563,0.00001897753,0.0001024151,0.00003220294,0.00005549823],"category_scores_gemma":[0.0004055404,0.00006686164,0.0002960967,0.0001477763,0.00007569313,0.0002297448,0.00001286264,0.00006128765,0.000001094475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008409982,"about_ca_system_score_gemma":0.001241315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003173081,"about_ca_topic_score_gemma":0.000007054082,"domain_scores_codex":[0.9985437,0.00003550915,0.0007559344,0.0001032962,0.0004900892,0.00007141862],"domain_scores_gemma":[0.9979656,0.000213519,0.0003110313,0.00008882185,0.001273836,0.0001471555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.06046084,0.008227572,0.1628536,0.002699452,0.004126635,0.0001680584,0.001097584,0.002597013,0.73226,0.004232637,0.004279209,0.01699734],"study_design_scores_gemma":[0.004690489,0.0004654997,0.8503779,0.001498522,0.0008235581,0.00002708108,0.0001483542,0.03030216,0.1093603,0.001117697,0.001080323,0.0001080713],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9788616,0.0002326552,0.01931548,0.0003848068,0.0004841708,0.000274665,0.000347181,0.00001179981,0.00008762578],"genre_scores_gemma":[0.9969479,0.00001542088,0.002530156,0.00002312827,0.000122592,0.00004457516,0.0002786026,0.00001079065,0.00002678594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6875243,"threshold_uncertainty_score":0.2726538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03492120550210275,"score_gpt":0.3169191136888416,"score_spread":0.2819979081867389,"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."}}