{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002536557,0.0003248611,0.0002998964,0.001097707,0.0003965466,0.0008925297,0.0005402589,0.0006979832,0.002590312],"category_scores_gemma":[0.03046237,0.0001328576,0.0002609267,0.0004902913,0.0003401734,0.0008679003,0.001340015,0.0007368374,0.0003926221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005757029,"about_ca_system_score_gemma":0.002688443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002398035,"about_ca_topic_score_gemma":0.002994696,"domain_scores_codex":[0.9972156,0.001166941,0.0003332647,0.0001460497,0.0009782861,0.0001598825],"domain_scores_gemma":[0.9759336,0.009056618,0.01100683,0.0006109799,0.002348962,0.001043032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001957149,0.0006687851,0.6997459,0.0006591321,0.00007392141,0.0001993601,0.0008058508,0.0009311907,0.001403084,0.0004226574,0.002580439,0.2923138],"study_design_scores_gemma":[0.00007755453,0.001220868,0.9774126,0.0009044533,0.0001176133,0.00172154,0.001228698,0.003361968,0.00297419,0.001673289,0.009266916,0.00004022459],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9510105,0.007822854,0.01230891,0.01400983,0.0001637542,0.0002844,0.0003023244,0.000275607,0.01382182],"genre_scores_gemma":[0.9847932,0.003131224,0.01011494,0.001031665,0.000263221,0.00007001746,0.0001403054,0.00000759209,0.0004478426],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002590312,"threshold_uncertainty_score":0.01341474,"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."}}