Paediatric emergency department overcrowding and adverse patient outcomes
Bibliographic record
Abstract
BACKGROUND: General emergency department crowding negatively impacts patient care, and increases patient morbidity. OBJECTIVES: This study seeks to determine if markers of paediatric emergency department (PED) flow are independently associated with negative outcomes and increased health care utilization. METHODS: We conducted a retrospective cohort study of PED visits from 2008 to 2012. Data were pulled from an electronic administrative database. Using multivariate logistic regression models, we measured the association between odds of adverse outcomes (hospital/paediatric intensive care unit [PICU] admission, unscheduled return visits and mortality) with markers of PED flow (shift mean length of stay [LOS] and daily rate of patients leaving without being seen [LWBS]). RESULTS: We found an association between the daily LWBS proportion and the odds of being admitted to the hospital (odds ratio [OR]: 2.1; 95% confidence interval [CI]: 1.2, 3.7), as well as admission to the PICU (OR: 8.9; 95% CI: 1.1, 71.3). We found a statistically significant increase in the odds of admission if seen during shifts in the third or fourth quartile mean shift LOS. We observed lower odds of returning to the PED with increased daily LWBS proportions (OR: 0.4; 95% CI: 0.2, 0.7), but found no association between the odds of returning to the PED and mean shift LOS. CONCLUSION: While we found an association between our pre-defined measures of adverse outcomes and markers of PED flow (or crowding), further studies are needed to determine whether PED overcrowding is the cause or effect of increased hospital and PICU admissions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".