The association between a prolonged stay in the emergency department and adverse events in older patients admitted to hospital: a retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: Patient safety studies have identified older adults as a high-risk group for adverse events (AEs). As frequent users of the emergency department (ED), they are vulnerable to the negative consequences of ED crowding. The study objective was to determine whether a prolonged ED stay is associated with an increased risk for the occurrence of AEs for older patients admitted to hospital. METHODS: This retrospective cohort study was conducted at the largest adult tertiary care facility in Atlantic Canada (1 July 2005-31 March 2006). All community-dwelling persons 65 years and older admitted to an acute inpatient unit from the ED were eligible. The exposure of interest was total length of stay (LOS) in the ED. The primary outcome was the occurrence of an AE in-hospital. AEs were identified from administrative diagnostic data using previously validated screening criteria. RESULTS: The average age of 982 eligible participants was 77.8 years (SD 7.8). The majority (75.0%) experienced a prolonged ED LOS as defined by national guidelines. There was evidence of at least one AE in 140 (14.3%) records. After adjustment, for every hour spent in the ED, the odds of experiencing an AE in-hospital increased 3% (OR 1.03, 95% CI 1.004 to 1.05). Those with an AE had twice the hospital LOS (20.2 vs 9.8 days, p < 0.00001). INTERPRETATION: A prolonged ED stay for older admitted patients is associated with an increased risk of an in-hospital AE. The longer hospital LOS associated with AEs further reduces the availability of acute care beds, thus exacerbating ED crowding.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".