{"id":"W3036835820","doi":"10.1016/j.clon.2020.05.015","title":"Optimising Medications for Patients With Cancer and Multimorbidity: The Case for Deprescribing","year":2020,"lang":"en","type":"article","venue":"Clinical Oncology","topic":"Pharmaceutical Practices and Patient Outcomes","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut Universitaire de Gériatrie de Montréal; Université de Montréal","funders":"Pharmacy Research UK; National Breast Cancer Foundation; Cancer Prevention and Research Institute of Texas; Canadian Institutes of Health Research; Mitacs; Blue Cross Blue Shield of Michigan Foundation","keywords":"Polypharmacy; Medicine; Deprescribing; Intensive care medicine; Multimorbidity; Cancer; Drug; Adverse effect; Beers Criteria; Health care; Chronic condition; Internal medicine; Psychiatry; Chronic disease; Disease","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000409355,0.00007447116,0.0002887904,0.00001089683,0.0001497576,0.000009412959,0.00005461729,0.00009782162,0.00003500424],"category_scores_gemma":[0.002604557,0.00004172603,0.0000698131,0.00005703252,0.0001853458,0.00005823254,0.00004243366,0.0002422148,0.0000016038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002428278,"about_ca_system_score_gemma":0.0001502928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002104127,"about_ca_topic_score_gemma":0.00002656654,"domain_scores_codex":[0.9991404,0.0000681422,0.0003370596,0.0002231511,0.00007249005,0.000158774],"domain_scores_gemma":[0.9930881,0.006221137,0.0001594833,0.0001002722,0.0001506105,0.0002803669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003730804,0.0009207153,0.5903554,0.0002258825,0.0006111267,0.00008954487,0.0005257037,0.00003151352,0.00001230963,0.0002557063,0.007054913,0.3961864],"study_design_scores_gemma":[0.03323356,0.0103048,0.08179206,0.00008152391,0.004048335,0.00009814457,0.0004692918,0.1187509,0.00003212221,0.0001145959,0.7508014,0.0002732231],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8810483,0.0002626014,0.005949876,0.1098572,0.0004417911,0.002073418,0.00006251458,0.00002930648,0.0002749375],"genre_scores_gemma":[0.9656575,0.0001364025,0.01076256,0.02272323,0.0004645202,0.0001821842,0.00002683349,0.00001409641,0.00003272511],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7437465,"threshold_uncertainty_score":0.3118086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4817498233976536,"score_gpt":0.5589120849746687,"score_spread":0.07716226157701511,"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."}}