{"id":"W3000753279","doi":"10.1002/rth2.12292","title":"Machine learning to predict venous thrombosis in acutely ill medical patients","year":2020,"lang":"en","type":"article","venue":"Research and Practice in Thrombosis and Haemostasis","topic":"Venous Thromboembolism Diagnosis and Management","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Foothills Medical Centre; University of Calgary","funders":"","keywords":"Medicine; Venous thromboembolism; Statistic; Venous thrombosis; Odds ratio; Logistic regression; Calibration; Confidence interval; Internal medicine; Randomized controlled trial; Thrombosis; Statistics; Mathematics","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":[0.004432742,0.0005257532,0.0007293737,0.0006553952,0.0001248326,0.0006427401,0.0003818102,0.0003614388,0.001096528],"category_scores_gemma":[0.01364549,0.0001514579,0.0006018844,0.000376644,0.000234817,0.0003853459,0.0005393506,0.001077671,0.0001922997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004229159,"about_ca_system_score_gemma":0.0006947566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008504872,"about_ca_topic_score_gemma":0.0007932698,"domain_scores_codex":[0.9982404,0.001226382,0.00009040686,0.0001416336,0.0002360732,0.00006509744],"domain_scores_gemma":[0.9928601,0.005444259,0.0007455901,0.0003457235,0.0003607828,0.0002436009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.007376921,0.0009906946,0.596315,0.0003023953,0.001997017,0.0001410065,0.0001392866,0.2187417,0.0009529394,0.001191748,0.00360586,0.1682455],"study_design_scores_gemma":[0.000792691,0.005423157,0.08964054,0.0001263898,0.000587797,0.0003273885,0.00007982794,0.8940136,0.00154199,0.006112032,0.001317615,0.00003688382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9720702,0.002353155,0.02195429,0.00144075,0.00007389521,0.0001075912,0.0004159687,0.0001146292,0.001469462],"genre_scores_gemma":[0.9934484,0.0004664022,0.005177127,0.0001379868,0.00005772366,0.00004246221,0.0004031132,0.000005826476,0.0002609957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004432742,"threshold_uncertainty_score":0.02344286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1450163074194829,"score_gpt":0.4253419002150256,"score_spread":0.2803255927955427,"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."}}