Hospital mortality among major trauma victims admitted on weekends and evenings: a cohort study
Bibliographic record
Abstract
BACKGROUND: Patient care may be inconsistent during off hours. We sought to determine whether victims of major trauma admitted to hospital on evenings, nights, and weekends suffer increased mortality rates. All victims of major trauma admitted to all four major acute care hospitals in the Calgary Health Region between April 1, 2002 and March 31, 2006 were included. Clinical and outcome information was obtained from regional databases. Weekends were defined as anytime Saturday or Sunday, evenings as 18:00-22:59, and nights as 23:00-07:59. RESULTS: Four thousand patients were included; 2,901 (73%) were male, the median age was 39.5 [inter-quartile range (IQR), 22.4-58.2] years, and the median injury severity score (ISS) was 20 (IQR, 16-26). Thirty-five percent (1,405) of patients were admitted on a weekend, 30% (1,197) during evenings, and 36% (1,422) at night. Seventy-eight percent (3,106) of cases presented during the "after hours" (evenings, nights, and/or weekends). The in-hospital case-fatality rate was 447 (11%), and was not significantly different during daytime (165/1,381; 37%), evening (128/1,197; 30%), and night (154/1,422; 36%) admissions (p = 0.53), or among patients admitted on weekends as compared to weekdays (157/1,405; 11% vs. 290/2,595; 11%; p = 1.0). Admission during the after hours as compared to business hours (343/3,106; 11% vs. 104/894; 12%; p = 0.63) did not increased risk. A multivariable logistic regression model was developed to assess factors associated with in-hospital death (n = 3,891). Neither admission on weekends nor on evenings or nights increased the risk for in-hospital mortality. CONCLUSION: In our region, the time of admission during the day or day of the week does not influence the risk for adverse outcome and may reflect our highly developed multi-hospital acute care and trauma system.
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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 | 0.001 |
| 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 | 0.001 | 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".