Quality indicators for the adult epilepsy monitoring unit
Bibliographic record
Abstract
OBJECTIVE: Quality and safety in epilepsy monitoring units (EMUs) are of great importance because patients' seizures are induced rather than prevented in this hospital setting. However, the measurement and evaluation of quality and safety in EMUs are heterogeneous, as are practices and processes of care. To improve the measurement of quality and safety in EMUs, we sought to develop evidence-based and consensus-driven quality indicators, adhering to previously described methodologic standards. METHODS: Candidate quality indicators were identified using a recent systematic review on quality and safety indicators in EMUs. These were supplemented by expert opinion to identify other indicators that had not been reported previously. The candidate quality indicators were then evaluated using a modified Delphi technique among a multidisciplinary EMU quality improvement team. Candidate indicators identified as important and feasible through the Delphi technique were then developed into quality metrics. RESULTS: Thirty-four candidate indicators were abstracted from 135 studies included in the earlier systematic review, and two additional candidate indicators were suggested through consensus from experts. Consensus was reached after two modified Delphi rounds for 25 quality indicators identified as important. These 25 indicators were then developed into quality metrics using a standardized data collection form and were deployed in an online database for systematic data capture and further analyses. SIGNIFICANCE: These quality indicators have the potential to improve the reporting of quality and safety in EMUs through standardized measurement and evaluation of the quality and safety of care. The ultimate goal is improved patient care and clinical outcomes through safer and better care for people with epilepsy in the EMU.
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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 | 0.095 | 0.247 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".