{"id":"W3187063758","doi":"10.2196/24651","title":"Candidemia Risk Prediction (CanDETEC) Model for Patients With Malignancy: Model Development and Validation in a Single-Center Retrospective Study","year":2021,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Korea Health Industry Development Institute","keywords":"Medicine; Random forest; Retrospective cohort study; Receiver operating characteristic; Logistic regression; Machine learning; Artificial intelligence; Cancer; Internal medicine; Algorithm; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004752672,0.0001958477,0.0002671187,0.0001117042,0.0002235181,0.00002489321,0.00009738432,0.0002368498,0.00004125886],"category_scores_gemma":[0.0001172481,0.0001686658,0.00003414818,0.0001998555,0.00007249606,0.0004027142,0.00007039489,0.0006576154,0.000004852002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000290587,"about_ca_system_score_gemma":0.0004168241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003117663,"about_ca_topic_score_gemma":0.00009783458,"domain_scores_codex":[0.9983656,0.00008155684,0.000624283,0.000193465,0.0004001786,0.0003349284],"domain_scores_gemma":[0.9991018,0.00009593503,0.0002270322,0.0001175622,0.0002020003,0.0002556246],"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":[0.0006296965,0.003823598,0.9374186,0.0001702752,0.0002672445,0.00001674891,0.02749055,0.0185124,0.00005898202,0.0000627042,0.00123179,0.01031741],"study_design_scores_gemma":[0.01144023,0.000210715,0.01715965,0.00009337423,0.0001249327,0.00001014335,0.001521849,0.9665456,0.0006392951,0.0001661992,0.001829747,0.0002582386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885175,0.00001210153,0.009174717,0.00006801217,0.0001637179,0.001222837,0.0002698015,0.00005443186,0.0005168879],"genre_scores_gemma":[0.9963665,0.00006210442,0.001957756,0.0006667218,0.00004185577,0.0005135938,0.0002352955,0.00001606055,0.0001400919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9480332,"threshold_uncertainty_score":0.6877993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0641049387357311,"score_gpt":0.380426749508448,"score_spread":0.3163218107727169,"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."}}