{"id":"W4393952833","doi":"10.1055/a-2299-4758","title":"Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review","year":2024,"lang":"en","type":"review","venue":"Thrombosis and Haemostasis","topic":"Venous Thromboembolism Diagnosis and Management","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; St. Joseph’s Healthcare Hamilton","funders":"H2020 Health","keywords":"Medicine; Audit; Receiver operating characteristic; MEDLINE; Systematic review; Clinical decision support system; Machine learning; Intensive care medicine; Medical physics; Artificial intelligence; Decision support system; Internal medicine; Computer science","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":[0.01035664,0.001838003,0.007219323,0.009056984,0.0004394728,0.00233378,0.002966115,0.001704736,0.004276907],"category_scores_gemma":[0.06273986,0.0008739978,0.01158655,0.008591499,0.0006506823,0.002611651,0.0010455,0.001726517,0.0003932236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002295331,"about_ca_system_score_gemma":0.007824946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00862625,"about_ca_topic_score_gemma":0.01511558,"domain_scores_codex":[0.9923464,0.003235427,0.002265131,0.0006279925,0.001401299,0.0001236987],"domain_scores_gemma":[0.9400111,0.05182148,0.004846317,0.0006450888,0.002467566,0.000208568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0002961482,0.00006121858,0.003627731,0.8297225,0.02444134,0.0001097988,0.0001343372,0.002057907,0.00007313034,0.0005975046,0.004002774,0.1348756],"study_design_scores_gemma":[0.0004612989,0.0003509889,0.008851564,0.8193041,0.1398052,0.0005286326,0.0001911541,0.003848915,0.0002273987,0.002096728,0.02420595,0.0001280043],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0008765473,0.996642,0.0008012982,0.0004352131,0.00008257126,0.0002007399,0.0007407056,0.0000251613,0.0001956827],"genre_scores_gemma":[0.02620131,0.9691898,0.002669472,0.0004592376,0.0001355372,0.0004547695,0.0008063554,0.00001214672,0.00007137875],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01035664,"threshold_uncertainty_score":0.05477178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05680966853769805,"score_gpt":0.3342754883825854,"score_spread":0.2774658198448873,"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."}}