{"id":"W6902059605","doi":"10.6084/m9.figshare.16530318","title":"Additional file 5 of Predicting postoperative surgical site infection with administrative data: a random forests algorithm","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Surgical site infection prevention","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; University of Ottawa; University of British Columbia; Ottawa Hospital","funders":"","keywords":"Random forest; Random error; Surgical site infection; Information system","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002137075,0.001025076,0.0009460548,0.001961427,0.0005907594,0.001478686,0.001878146,0.001201257,0.7194441],"category_scores_gemma":[0.03145812,0.000543161,0.001274598,0.002835706,0.0002092441,0.001232549,0.0008186873,0.001078503,0.1288789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000772518,"about_ca_system_score_gemma":0.001164728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006883455,"about_ca_topic_score_gemma":0.01022373,"domain_scores_codex":[0.9991755,0.0002040202,0.0001404961,0.0002391937,0.0001344158,0.0001064011],"domain_scores_gemma":[0.9820367,0.01420263,0.0007647528,0.001174055,0.001540369,0.0002815408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003887944,0.0001236938,0.006692198,0.0009028041,0.00009918046,0.00007792859,0.0000334603,0.001484734,0.00007751226,0.0005277822,0.978353,0.01123894],"study_design_scores_gemma":[0.00831137,0.0006054287,0.07296335,0.003115103,0.0005637493,0.0009200366,0.0005142849,0.02321586,0.002196654,0.02245023,0.8648899,0.0002540578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0004362648,0.00001439582,0.0004234693,0.00008487765,0.00002072221,0.00004647368,0.9980966,0.0003774582,0.0004998083],"genre_scores_gemma":[0.01041465,0.00006498975,0.004074472,0.0002595976,0.00008267781,0.0007597137,0.9795686,0.0005969186,0.004178414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7194441,"threshold_uncertainty_score":0.4001789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04905975902173149,"score_gpt":0.3150198700481506,"score_spread":0.2659601110264191,"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."}}