{"id":"W2073336303","doi":"10.1097/01.nur.0000311795.69476.2f","title":"Reducing Preventable Medication Safety Events by Recognizing Renal Risk","year":2008,"lang":"en","type":"article","venue":"Clinical Nurse Specialist","topic":"Patient Safety and Medication Errors","field":"Health Professions","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fields Institute for Research in Mathematical Sciences","funders":"Agency for Healthcare Research and Quality","keywords":"Medicine; Renal function; Clearance; Intensive care medicine; Dosing; Pharmacist; Risk assessment; Patient safety; Emergency medicine; Internal medicine; Pharmacy; Health care; Nursing; Urology","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":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.003203971,0.0002003576,0.000487257,0.00007144163,0.001797251,0.000001055148,0.0003477646,0.0004566501,0.003229226],"category_scores_gemma":[0.005777034,0.0001866186,0.0001910084,0.0002991443,0.0003608866,0.000218741,0.00006888635,0.001457458,0.001825215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002609828,"about_ca_system_score_gemma":0.0005860181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002797809,"about_ca_topic_score_gemma":0.00007046336,"domain_scores_codex":[0.9943892,0.001794082,0.002079024,0.0005953109,0.000560748,0.0005815703],"domain_scores_gemma":[0.9959109,0.001429326,0.001357336,0.0005940587,0.0001696696,0.0005386714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004857694,0.0007222425,0.2592126,0.00004414793,0.00008440916,0.000006168211,0.002372664,0.0000125575,0.00006244165,0.0004394274,0.7077246,0.02883304],"study_design_scores_gemma":[0.002216004,0.0001026942,0.268238,0.0003882315,0.00007342936,0.000003148052,0.0005283254,0.00008260359,0.00002379469,0.0006048869,0.7275218,0.0002171199],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6723366,0.0007087847,0.01984006,0.03014524,0.02990093,0.004044021,0.0008802276,0.0007512805,0.2413928],"genre_scores_gemma":[0.9041927,0.008024832,0.003758786,0.005707143,0.01104139,0.000276555,0.002114083,0.0001189781,0.06476557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.231856,"threshold_uncertainty_score":0.9995023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1245871262390564,"score_gpt":0.4685974507580025,"score_spread":0.3440103245189461,"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."}}