{"id":"W3096195021","doi":"10.1177/2054358120968674","title":"Development and Validation of Nine Deprescribing Algorithms for Patients on Hemodialysis to Decrease Polypharmacy","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Kidney Health and Disease","topic":"Health Education and Validation","field":"Social Sciences","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Providence Health Care; University of Alberta; University Health Network; Health Sciences Centre; University of British Columbia; Nova Scotia Health Authority; Western University; University of Toronto; Dalhousie University; University of Calgary; Institute for Clinical Evaluative Sciences","funders":"Canadian Institutes of Health Research; Kidney Foundation of Canada","keywords":"Polypharmacy; Deprescribing; Medicine; Hemodialysis; Algorithm; Intensive care medicine; Internal medicine; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.000451069,0.0000574937,0.0001307167,0.0001624629,0.0003792392,0.00003556825,0.00005584023,0.00001840033,0.00005137524],"category_scores_gemma":[0.03169585,0.00005833511,0.00002459896,0.0001631717,0.0000269236,0.0001141078,0.000003426302,0.00004340532,0.000001017896],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000145622,"about_ca_system_score_gemma":0.05183665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00385079,"about_ca_topic_score_gemma":0.0004462511,"domain_scores_codex":[0.9990869,0.0001013115,0.0003392739,0.0001008572,0.0001605779,0.0002110688],"domain_scores_gemma":[0.9528749,0.00005878301,0.000190104,0.00003422698,0.0002210769,0.04662091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000829486,0.0001892033,0.2686504,0.0007650309,0.00003794314,0.000005135842,0.1108298,0.00002639138,0.000009402323,0.001595489,0.2795872,0.3374745],"study_design_scores_gemma":[0.002117031,0.0003302252,0.01127412,0.0002145505,0.00003918598,2.736171e-7,0.003635554,0.0001378811,0.000121203,0.000109553,0.9818453,0.00017519],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6961165,0.0002978563,0.0008201472,0.3015237,0.0003663313,0.0006707227,0.0001156623,0.000004750875,0.00008435035],"genre_scores_gemma":[0.8673456,0.00007298901,0.003498493,0.1287419,0.0002449063,0.00001072135,0.00005742772,0.00000739716,0.00002055672],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7022581,"threshold_uncertainty_score":0.9764606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09626288541353652,"score_gpt":0.3775168427107053,"score_spread":0.2812539572971688,"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."}}