{"id":"W4413318411","doi":"10.1055/a-2669-7933","title":"Machine Learning in Venous Thromboembolism—Why and What Next?","year":2025,"lang":"en","type":"review","venue":"Seminars in Thrombosis and Hemostasis","topic":"Venous Thromboembolism Diagnosis and Management","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Lawrence College; Queen's University","funders":"","keywords":"Medicine; Intensive care medicine; Prospective cohort study; Framingham Risk Score; Pulmonary embolism; Deep vein; Venous thrombosis; Risk assessment; Machine learning; Thrombosis; Disease; Internal medicine","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":[0.01266005,0.000638918,0.001305272,0.001929642,0.00082768,0.005389269,0.001824134,0.00471349,0.004296768],"category_scores_gemma":[0.03863499,0.0003999355,0.0006811402,0.002211357,0.004743437,0.01072675,0.001704723,0.00935517,0.002123284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002341899,"about_ca_system_score_gemma":0.00275153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003925694,"about_ca_topic_score_gemma":0.003113211,"domain_scores_codex":[0.9951118,0.003273587,0.000224352,0.0003531104,0.0007884359,0.0002488115],"domain_scores_gemma":[0.963956,0.02607479,0.001345689,0.00114881,0.005611413,0.001863316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001580391,0.0001314241,0.006850149,0.001987713,0.0001755924,0.0001250226,0.0004022111,0.003134644,0.0001648747,0.08446867,0.2681173,0.6342844],"study_design_scores_gemma":[0.000075143,0.0001908791,0.007108089,0.01112386,0.0001282763,0.0003513445,0.001013263,0.01636157,0.0004207947,0.5033939,0.4596714,0.0001614447],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"review","genre_scores_codex":[0.002476279,0.3818205,0.01180223,0.5917448,0.007839946,0.00003057449,0.0002146488,0.000153535,0.003917452],"genre_scores_gemma":[0.1123253,0.7026055,0.02558547,0.09600119,0.05578112,0.0002574518,0.0004743536,0.0002316678,0.006737997],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01266005,"threshold_uncertainty_score":0.06695348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0496584401841524,"score_gpt":0.347291993985182,"score_spread":0.2976335538010296,"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."}}