{"id":"W4414902723","doi":"10.2196/68710","title":"Probing the Relationship Between Perioperative Complications in Patients With Valvular Heart Disease: Network Analysis Based on Bayesian Network","year":2025,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Cardiac, Anesthesia and Surgical Outcomes","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Perioperative; Bayesian network; Identification (biology); Network analysis; valvular heart disease","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002623206,0.000756154,0.0007472539,0.00443075,0.0008514998,0.001395716,0.0009127846,0.0009464037,0.001981751],"category_scores_gemma":[0.01144839,0.0004765229,0.001426932,0.002193891,0.0006396467,0.001640891,0.001049444,0.001007134,0.0001580304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001700272,"about_ca_system_score_gemma":0.001463628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02164367,"about_ca_topic_score_gemma":0.02179545,"domain_scores_codex":[0.99835,0.0008511528,0.0001028378,0.0003979075,0.0001612801,0.000136792],"domain_scores_gemma":[0.993247,0.00510513,0.000914447,0.0001451154,0.0003809938,0.0002072204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002724386,0.0002094914,0.2694221,0.0004135811,0.0009365784,0.0007164387,0.0006850791,0.6491445,0.0008258518,0.01816274,0.003562813,0.05564849],"study_design_scores_gemma":[0.00001544736,0.00004486664,0.01838155,0.00006295351,0.0002195808,0.0001613029,0.0001660884,0.958635,0.0001543666,0.0211989,0.0009314407,0.00002849738],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5538169,0.002778866,0.4308778,0.003218586,0.0001051868,0.0003215792,0.00417342,0.0002461698,0.00446154],"genre_scores_gemma":[0.9516298,0.001233111,0.04328916,0.0001471696,0.00007535027,0.0002157553,0.002220202,0.00001980115,0.001169594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02164367,"threshold_uncertainty_score":0.04303539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03865294135385237,"score_gpt":0.3753332566595127,"score_spread":0.3366803153056603,"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."}}