California Emergency Department Closures Are Associated With Increased Inpatient Mortality At Nearby Hospitals
Bibliographic record
Abstract
Between 1996 and 2009 the annual number of emergency department (ED) visits in the United States increased by 51 percent while the number of EDs nationwide decreased by 6 percent, which placed unprecedented strain on the nation's EDs. To investigate the effects of an ED's closing on surrounding communities, we identified all ED closures in California during the period 1999-2010 and examined their association with inpatient mortality rates at nearby hospitals. We found that one-quarter of hospital admissions in this period occurred near an ED closure and that these admissions had 5 percent higher odds of inpatient mortality than admissions not occurring near a closure. This association persisted whether we considered ED closures as affecting all future nearby admissions or only those occurring in the subsequent two years. These results suggest that ED closures have ripple effects on patient outcomes that should be considered when health systems and policy makers decide how to regulate ED closures.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".