Emergency Department Conditions Associated With the Number of Patients Who Leave a Pediatric Emergency Department Before Physician Assessment
Bibliographic record
Abstract
OBJECTIVES: As emergency department (ED) waiting times and volumes increase, substantial numbers of patients leave without being seen (LWBS) by a physician. The objective of this study was to identify ED conditions reflecting patient input, throughput, and output associated with the number of patients who LWBS in a pediatric setting. METHODS: This study was a retrospective, descriptive study using data from 1 urban, tertiary care pediatric ED. The study population consisted of all patient visits to the ED from April 2005 to March 2007. Multivariate Poisson regression analyses were used to examine the impact of the timing of patient arrival and ED conditions including patient acuity, volume, and waiting times on the number of patients who LWBS. RESULTS: During the study period, there were 138,361 patient visits corresponding to 2190 consecutive shifts; 11,055 patients (8%) left without being seen by a physician.In the multivariate analysis, the throughput variables, time from triage to physician assessment (rate ratio, 2.11; 95% confidence interval, 2.01-2.21), and time from registration to triage (rate ratio, 1.55; 95% confidence interval, 1.25-1.90) had the largest association with the number of patients who LWBS. CONCLUSIONS: In the study ED, throughput variables played a more important role than input or output variables on the number of patients who LWBS. This finding, which contrasts with a work done previously in an ED serving primarily adults, highlights the importance of pediatric specific research on the impacts of increasing ED waiting times and volumes.
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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 | 0.000 | 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".