Effects of fast-track in a university emergency department through the National Emergency Department Overcrowding Study.
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of a fast track area on emergency department crowding and its efficacy for non-urgent patients. METHODS: The prospective cross-sectional study was conducted in an adult emergency department of a university-affiliated hospital in Turkey from September 17 to 30, 2010. Non-urgent patients were defined as those with Canadian Triage Acuity Scale category 4/5. The fast track area was open in the emergency department for one whole week, followed by another week in which fast track area was closed. Demographic information of patients, their complaints on admission, waiting times, length of stay and revisits were recorded. Overcrowding evaluation was performed via the National Emergency Department Overcrowding Study scale. In both weeks, the results of the patients were compared and the effects of fast track on the results were analysed. Continuous variables were compared via student's t test or Mann Whitney U test. Demographic features of the groups were evaluated by chi-square test. RESULTS: A total of 249 patients were seen during the fast track week, and 239 during the non-fast track week at the emergency department. Satisfaction level was higher in the fast track group than the non-fast track group (p < 0.001). The waiting times shortened from 20 minutes to 10 minutes and length of stay shortened from 80 minutes to 42 minutes during the fast track week. Morbidity and mortality rates remained unchanged. CONCLUSION: Owing to fast track, overcrowding in the emergency department was lessened. It also improved effectiveness and quality measures.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".