{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002700918,0.00026207,0.0008326552,0.0004397824,0.0002527213,0.0001483625,0.0001986515,0.0002013239,0.0003731117],"category_scores_gemma":[0.01017887,0.0002322681,0.00005251711,0.001152108,0.0002061265,0.0004047851,0.0005956113,0.001260541,0.0000562686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001215914,"about_ca_system_score_gemma":0.000160173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002494805,"about_ca_topic_score_gemma":0.0003814631,"domain_scores_codex":[0.9958069,0.0006513114,0.0005333682,0.0007606409,0.001432924,0.0008148202],"domain_scores_gemma":[0.9965786,0.001753205,0.00008717013,0.0002438156,0.0003244878,0.001012719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003055494,0.01123118,0.524254,0.001368946,0.0009175416,0.000667571,0.0362557,0.00003883597,0.0003677043,0.001384312,0.02522956,0.3952292],"study_design_scores_gemma":[0.006778111,0.01045325,0.7837375,0.000946545,0.0002271748,0.00002899124,0.008258414,0.001366446,0.0002079762,0.000195624,0.1872618,0.0005381237],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.874221,0.001849435,0.00001525248,0.1153604,0.00005918786,0.001428982,0.00003222491,0.00004917242,0.006984259],"genre_scores_gemma":[0.9446357,0.04723171,0.0006805211,0.007112734,0.0001109519,0.0001145616,0.0000465642,0.00004492948,0.00002236483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3946911,"threshold_uncertainty_score":0.9981588,"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."}}