Incidence of medication errors and adverse drug events in the ICU: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it