Reducing Preventable Medication Safety Events by Recognizing Renal Risk
Bibliographic record
Abstract
UNLABELLED: Evidence suggests that medication safety may be improved through more accurate assessment of renal function and appropriate dosing of renally cleared medications. The purpose of this article is to describe patient renal risk groups, associated medication errors, and ways that nurses can improve renal assessment. METHODS: Medication safety data were collected through voluntary reporting, computerized triggers, pharmacist surveillance, and retrospective chart review. Data were analyzed across 3 renal risk groups. RESULTS: Findings indicated that regarding the detected medication errors, elderly women were more likely to have hidden renal risk and that prescribing errors involving a wrong dose occurred more often in patients with high and hidden renal risk. Antibiotic and diabetic medications were the primary drug categories involved in these medication errors. RECOMMENDATIONS: Results indicated that identification of patients with hidden renal risk can be improved by routinely assessing serum creatinine and estimated creatinine clearance levels during renal assessments. Clinical nurse specialists can use this evidence to promote safer nursing care of renal patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".