The impact of time of admission on major complications and mortality in patients undergoing emergency trauma surgery
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown a relationship between time of admission to hospital and mortality rates; however, it is uncertain whether such a relationship exists for patients requiring emergency trauma surgery. METHODS: We included all trauma patients, except those with moderate to severe burns, who presented to a university-affiliated level 1 trauma center and underwent surgery, from 1995 until 2001 (n = 1044). We conducted univariate and multivariate analyses in which the dependent variables were in-hospital mortality and major complications, and the independent variables were the time of presentation to the trauma centre (nighttime vs. daytime, weekend vs. weekday, month of year, and year), age, sex, injury severity score, type of operative procedure, and total number of operative procedures. RESULTS: None of the factors related to time of presentation were associated with major complications or mortality. Factors predictive of increased mortality were higher ISS (odds ratio 1.07; 95% confidence interval 1.03-1.08), older age (1.04; 1.03-1.07), operations involving the cardiovascular system (1.7; 1-2.6), "miscellaneous" operative procedures (1.8; 1.1-2.9), and major complications (2.4; 1.4-4.2). INTERPRETATION: Time of presentation for emergency trauma surgery was not associated with differences in major complications or in mortality.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".