Quality and safety in adult epilepsy monitoring units: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: The epilepsy monitoring unit (EMU) is a valuable resource for optimizing management of persons with epilepsy, but may place patients at risk for adverse events due to withdrawal of treatment and induction of symptoms. The purpose of this study was to synthesize data on the safety and quality of care in EMUs to inform the development of quality indicators for EMUs. METHODS: A systematic review was conducted according to the Preferred Reporting and Items for Systematic Review and Meta-Analysis (PRISMA) statement. The search strategy, which included broad search terms and synonyms pertaining to the EMU, was run in six medical databases and included conference proceedings. Data abstracted included patient and EMU demographics and quality and safety variables. Study quality was evaluated using a modified 15-item Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist. Descriptive statistics and meta-analyses were used to describe and synthesize the evidence. RESULTS: The search yielded 7,601 references, of which 604 were reviewed in full text. One-hundred thirty-five studies were included. The quality and safety data came from 181,823 patients and reported on 34 different quality and safety variables. Included studies commonly reported the number of patients (108 studies; median number patients, 171.5), age (49 studies; mean age 35.7 years old), and the reason for admission (34 studies). The most common quality and safety data reported were the utility of the EMU admission (38 studies). Thirty-three studies (24.4%) reported on adverse events, and yielded a pooled proportion of adverse events of 7% (95% confidence interval [CI] 5-9%). The mean quality score was 73.3% (standard deviation [SD] 17.2). SIGNIFICANCE: This study demonstrates that there is a great deal of variation in the reporting of quality and safety measures and in the quality and safety in EMUs. Study quality also varied considerably from one study to the next. These findings highlight the need to develop evidence-based, consensus-driven quality indicators for EMUs.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".