{"id":"W4398196299","doi":"10.1007/s00464-024-10926-4","title":"Machine learning-based preoperative analytics for the prediction of anastomotic leakage in colorectal surgery: a swiss pilot study","year":2024,"lang":"en","type":"article","venue":"Surgical Endoscopy","topic":"Colorectal Cancer Surgical Treatments","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Medtronic Foundation; Universität Basel","keywords":"Logistic regression; Random forest; Medicine; Anastomosis; Colorectal surgery; Receiver operating characteristic; Surgery; Test set; Linear regression; Cross-validation; Machine learning; Internal medicine; Computer science; Abdominal surgery","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":[0.0006768809,0.0002570338,0.0006755763,0.0002588675,0.00008382506,0.00003451958,0.00009073735,0.00006222285,0.0002209282],"category_scores_gemma":[0.0003765475,0.0001620772,0.0002351268,0.00101092,0.0001456977,0.00006591323,0.00003994018,0.0004250532,0.00000840028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002771957,"about_ca_system_score_gemma":0.0003074171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003561677,"about_ca_topic_score_gemma":0.0003383853,"domain_scores_codex":[0.9979604,0.0002478978,0.0005368053,0.0004617343,0.0004506373,0.0003425101],"domain_scores_gemma":[0.9959958,0.003501303,0.00007454071,0.0001954367,0.0001058504,0.0001270525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.02664934,0.006064622,0.9539572,0.0005503747,0.001100408,0.001444735,0.0009236737,0.006024311,0.0004197811,0.0002229721,0.0001468886,0.0024957],"study_design_scores_gemma":[0.03370611,0.0513716,0.3718192,0.000901748,0.001504771,0.0001022062,0.0005644026,0.5262153,0.008355814,0.000114566,0.004883971,0.0004604048],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942498,0.001582382,0.0002229212,0.0004802973,0.0003736259,0.0023471,0.000114428,0.0001382946,0.0004911398],"genre_scores_gemma":[0.9988252,0.00008056209,0.00004007897,0.00001978338,0.0001016657,0.0004848988,0.00009214978,0.00004235049,0.000313323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.582138,"threshold_uncertainty_score":0.6609314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04618967490734713,"score_gpt":0.320774702595127,"score_spread":0.2745850276877799,"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."}}