{"id":"W4387491890","doi":"10.2139/ssrn.4591012","title":"Exploring Surgical Infection Prediction: A Comparative Study of Established Risk Indexes and a Novel Model","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Surgical site infection; Computer science; Econometrics; Medicine; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008539812,0.001266712,0.001242897,0.003217782,0.000386149,0.002678757,0.001455739,0.001055081,0.001368739],"category_scores_gemma":[0.01928432,0.0001972044,0.001747937,0.001890193,0.0005061149,0.002188596,0.001132412,0.001088121,0.0003210911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005997007,"about_ca_system_score_gemma":0.001178501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002954794,"about_ca_topic_score_gemma":0.0016671,"domain_scores_codex":[0.9980372,0.0009123545,0.000175921,0.000416153,0.0003258088,0.000132683],"domain_scores_gemma":[0.971449,0.02506687,0.001111628,0.0009098504,0.0009907985,0.0004718376],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00508567,0.00166391,0.8031788,0.0004392299,0.002322607,0.0002893226,0.0003709169,0.05652399,0.001170451,0.001949176,0.00132979,0.1256762],"study_design_scores_gemma":[0.0001973874,0.005373118,0.2301459,0.0001174468,0.002470121,0.0006705268,0.0008590944,0.7517431,0.001571424,0.005834308,0.0008967495,0.0001208509],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9637076,0.002735506,0.03070958,0.0004136249,0.0001027594,0.00008326054,0.0007602487,0.000182746,0.001304672],"genre_scores_gemma":[0.992017,0.0006330226,0.006305619,0.00003097742,0.00008063248,0.00002633119,0.0006865961,0.00002670772,0.000193159],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008539812,"threshold_uncertainty_score":0.04516339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1418082364337505,"score_gpt":0.3399311181856497,"score_spread":0.1981228817518992,"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."}}