The role of a rapid assessment zone/pod on reducing overcrowding in emergency departments: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of a rapid assessment zone (RAZ) to mitigate emergency department (ED) overcrowding. METHODS: Electronic databases, controlled trial registries, conference proceedings, study references, experts in the field and correspondence with authors were used to identify potentially relevant studies. Intervention studies, in which a RAZ was used to influence length of stay, physician initial assessment and patients left without being seen, were included. Mean differences were calculated and reported with corresponding 95% CIs; individual statistics are presented as RR with associated 95% CI. RESULTS: From 14 446 potentially relevant studies, four studies were included in the review. The quality of one study was appraised as moderately high; others were rated as weak. Two studies showed that a RAZ was associated with a reduction of 20 min (95% CI: -47.2 to 7.2) in the ED length of stay; in one non-randomised clinical trial (RCT), a 192 min reduction was reported (95% CI: -211.6 to -172.4). Physician initial assessment showed a reduction of 8.0 min; 95% CI: -13.8 to -2.2 in the RCT and a reduction of 33 min (95% CI: -42.3 to -23.6) and 18 min (95% CI: -22.2 to -13.8) respectively were found in two non-RCTs. There was a reduction in the risk of patient leaving without being seen (RCT: RR=0.93, 95% CI: 0.77 to 1.12; non-RCT: RR =0.68, 95% CI: 0.63 to 0.73). CONCLUSIONS: Although the results are consistent, and low acuity patients seem to benefit the most from a RAZ, the available evidence to support its implementation is limited.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".