Does emergency medicine length of stay predict trauma outcomes at a Level 1 Trauma Center?
Bibliographic record
Abstract
Objective: Previous studies looking at emergency department (ED) crowding and delays of care on outcome measures for certain medical and surgical patients excluded trauma patients. The objectives of this study were to assess the relationship of trauma patients’ ED length of stay (EDLOS) on hospital length of stay (HLOS) and on mortality; and to examine the association of ED and hospital capacity on EDLOS.Methods: This was a retrospective database review of Level 1 and 2 trauma patients at a single site Level 1 Trauma Center in the Midwest over a one year period. Out of a sample of 1,492, there were 1,207 patients in the analysis after exclusions. The main outcome was the difference in hospital mortality by EDLOS group (short was less than 4 hours vs. long, greater than 4 hours). HLOS was compared by EDLOS group, stratified by Trauma Injury Severity Score (TRISS) category (< 0.5, 0.51-0.89, > 0.9) to describe the association between ED and hospital capacity on EDLOS.Results: There was no significant difference in mortality by EDLOS (4.8% short and 4% long, p = .5). There was no significant difference in HLOS between EDLOS, when adjusted for TRISS. ED census did not affect EDLOS (p = .59), however; EDLOS was longer when the percentage of staffed hospital beds available was lower (p < .001).Conclusions: While hospital overcrowding did increase EDLOS, there was no association between EDLOS and mortality or HLOS in leveled trauma patients at this institution.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".