Incidence of medication errors and adverse drug events in the ICU: a systematic review
Bibliographic record
Abstract
BACKGROUND: Medication errors (MEs) and adverse drug events (ADEs) are both common and under-reported in the intensive care setting. The definitions of these terms vary substantially in the literature. Many methods have been used to estimate their incidence. METHODS: A systematic review was done to assess methods used for tracking unintended drug events in intensive care units (ICUs). Studies published up to 22 June 2007 were identified by searching eight online databases, including Medline. In total, 613 studies were evaluated for inclusion by two reviewers. RESULTS: The authors selected 29 papers to analyse; all studies took place in an ICU, were reproducible and reported ICU-specific rates of events. Rates of MEs varied from 8.1 to 2344 per 1000 patient-days, and ADEs from 5.1 to 87.5 per 1000 patient-days. The definitions of ADE and ME in the studies varied widely. CONCLUSIONS: Much variation exists in reported rates and definitions of ADEs and MEs in ICUs. Some of this variation may be due to a lack of standard definitions for ADEs and MEs, and methods for detecting them. Further standardisation is needed before these methods can be used to evaluate process improvements.
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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 | 0.013 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".