The effect of a zero-diversion policy on emergency department performance measures
Bibliographic record
Abstract
This study examines how emergency department (ED) performance measures at an academic tertiary care center in the Midwest were affected by a regionally-adopted zero diversion policy. Two six-month periods before and after the policy was enacted were selected to measure differences in key performance measures, including left without treatment (LWOT), left without being seen (LWBS), left against medical advice (AMA), mortality, length of stay and hospital admission rate. Total ED census during the two periods was similar. While the zero diversion policy was in effect, LWOT and LWBS rates were 19.4% and 18.2% lower, respectively, than the prior period, p < .002; discharged patients had faster treatment times (228 + 8.0 minutes vs. 242 + 9.0 minutes), p = .015. No differences were observed in AMA or mortality rates. This study revealed no worsening of ED performance measures after adoption of a zero diversion policy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".