{"id":"W4402987528","doi":"10.1200/op.2024.20.10_suppl.409","title":"Impact of applying machine learning to the electronic medical record on prediction of cancer-associated thrombosis.","year":2024,"lang":"en","type":"article","venue":"JCO Oncology Practice","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Health Science Centre; University of Toronto; Princess Margaret Cancer Centre; Health Sciences Centre; University Health Network","funders":"Princess Margaret Cancer Foundation; TD Bank","keywords":"Thrombosis; Cancer; Electronic medical record; Medical record; Machine learning; Artificial intelligence; Computer science; Medicine; Medical emergency; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01058458,0.0007490388,0.00063566,0.002598768,0.0003942586,0.001679437,0.0007106054,0.001008649,0.0006919189],"category_scores_gemma":[0.04294158,0.0003431758,0.001075946,0.001150584,0.0003038116,0.0014639,0.001009647,0.001261046,0.0005647902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001459729,"about_ca_system_score_gemma":0.00125662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01619822,"about_ca_topic_score_gemma":0.01599535,"domain_scores_codex":[0.9946454,0.002806034,0.0006682168,0.001158397,0.000562134,0.0001598806],"domain_scores_gemma":[0.9474775,0.04316716,0.003753467,0.001882144,0.003038998,0.0006807967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001043354,0.0006826573,0.7580826,0.0003068656,0.0007272558,0.0002274498,0.0001970145,0.07792751,0.001391995,0.0001858675,0.004709946,0.1545175],"study_design_scores_gemma":[0.00005826466,0.0006386453,0.09517843,0.0001164502,0.0001913255,0.0002306743,0.000104801,0.8991899,0.001820745,0.000791378,0.001631564,0.00004777547],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9561892,0.003050904,0.02425822,0.003951484,0.0003456065,0.0002791351,0.007220372,0.00259725,0.002107875],"genre_scores_gemma":[0.9752898,0.0002716892,0.01797377,0.0003983489,0.0001422081,0.00005398331,0.005473254,0.00002780832,0.0003691541],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01619822,"threshold_uncertainty_score":0.05597728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1587365689895234,"score_gpt":0.5772817834033115,"score_spread":0.4185452144137881,"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."}}