{"id":"W4391535871","doi":"10.1038/s41598-024-52944-1","title":"Predicting outcomes following lower extremity open revascularization using machine learning","year":2024,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Peripheral Artery Disease Management","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Medical Association; University Health Network; Artificial Intelligence in Medicine (Canada); University of Toronto; St. Michael's Hospital","funders":"Canadian Institutes of Health Research; Physicians' Services Incorporated Foundation; Ontario Ministry of Health and Long-Term Care; Brigham and Women's Hospital","keywords":"Revascularization; Medicine; Brier score; Receiver operating characteristic; Adverse effect; Arterial disease; Amputation; Surgery; Machine learning; Internal medicine; Vascular disease; Computer science","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":[0.003603363,0.0006364905,0.0005508625,0.001261885,0.0001809967,0.0007905421,0.0004993201,0.0004163206,0.0006366143],"category_scores_gemma":[0.01121131,0.0001741701,0.0004694453,0.0005850981,0.000248087,0.0004777908,0.0005145572,0.0007276232,0.0002376144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004646726,"about_ca_system_score_gemma":0.0006439494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003060155,"about_ca_topic_score_gemma":0.003292443,"domain_scores_codex":[0.9989053,0.0005890796,0.00008640904,0.0001799257,0.0001417883,0.00009743372],"domain_scores_gemma":[0.9934768,0.004471485,0.001089888,0.0002392705,0.0004973536,0.0002252345],"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":[0.0007216145,0.0006472117,0.7702844,0.00006315083,0.0002582464,0.0001566124,0.0001015969,0.1450695,0.0006457363,0.0002348805,0.001193491,0.08062367],"study_design_scores_gemma":[0.00005699147,0.000689224,0.1710545,0.00003848763,0.00007390663,0.0001621397,0.00006761789,0.8243059,0.001338678,0.001822723,0.0003626517,0.00002718489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9747382,0.0002770885,0.02320274,0.0002851351,0.00003154971,0.00007442778,0.0005383824,0.0001925979,0.0006597392],"genre_scores_gemma":[0.9928169,0.00008059351,0.006051138,0.00004412152,0.00003255866,0.00003612662,0.0007754136,0.000007864803,0.0001552275],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003603363,"threshold_uncertainty_score":0.01905668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02886659974245539,"score_gpt":0.3129844713279193,"score_spread":0.284117871585464,"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."}}