{"id":"W3012136184","doi":"10.1245/s10434-020-08307-x","title":"Development and Evaluation of a Machine Learning Prediction Model for Flap Failure in Microvascular Breast Reconstruction","year":2020,"lang":"en","type":"article","venue":"Annals of Surgical Oncology","topic":"Reconstructive Surgery and Microvascular Techniques","field":"Medicine","cited_by":64,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"","keywords":"Medicine; Machine learning; Decision tree; Cohort; Artificial intelligence; Random forest; Retrospective cohort study; Resampling; Surgery; Internal medicine; Computer science","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":[0.003096483,0.0009220435,0.001020565,0.00094262,0.0004364713,0.001065136,0.001426891,0.001143328,0.001436604],"category_scores_gemma":[0.006365353,0.0003814612,0.0006824571,0.0004597904,0.0002160178,0.0007401148,0.0005980742,0.001606884,0.0005042949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009371761,"about_ca_system_score_gemma":0.001379887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01919362,"about_ca_topic_score_gemma":0.01229783,"domain_scores_codex":[0.9994426,0.0001944723,0.00006440159,0.0001358445,0.00009379272,0.00006877169],"domain_scores_gemma":[0.9948836,0.003730635,0.0001979294,0.0001272577,0.0009403781,0.0001201263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005841529,0.001138513,0.03296585,0.0001013221,0.0002599568,0.0001670219,0.00006247845,0.7569562,0.001989741,0.000704294,0.003159431,0.2019111],"study_design_scores_gemma":[0.000006386365,0.00003333784,0.000632944,0.000002485417,0.000009188755,0.000006741455,0.000005188539,0.9989367,0.0002225296,0.00009629858,0.00004594658,0.00000230675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6770124,0.001236651,0.3137779,0.001288561,0.000274528,0.0004243936,0.001203416,0.002410715,0.002371368],"genre_scores_gemma":[0.9406988,0.0002051898,0.05621514,0.0001015364,0.0000593685,0.0001902849,0.001252355,0.00004554338,0.001231819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01919362,"threshold_uncertainty_score":0.03816378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0824593212240239,"score_gpt":0.3418830637804161,"score_spread":0.2594237425563922,"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."}}