Definition of mortality for trauma center performance evaluation: A comparative study*
Bibliographic record
Abstract
OBJECTIVE: Mortality is widely used as a performance indicator to evaluate the quality of trauma care, but there is no consensus on the most appropriate definition. Our objective was to evaluate the influence of the definition of mortality in terms of the place (in-hospital or postdischarge) and time (30 days and 3, 6, and 12 months) of death on the results of trauma center performance evaluations according to the patients' ages. DESIGN: Multicenter retrospective cohort study. SETTING: Inclusive Canadian provincial trauma system. PATIENTS: Adults admitted between 1999 and 2006 with a maximum abbreviated injury severity score≥3 (n=47,261). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Trauma registry data were linked to vital statistics data to obtain mortality up to 12 months postadmission. Observed mortality was compared to that expected according to provincial population mortality rates. Trauma center performance was evaluated with risk-adjusted mortality estimates. Agreement between performance results based on different definitions of mortality was evaluated with correlation coefficients; >.9 was considered acceptable. Analyses were stratified by predefined age categories (16-64, 65-84, and ≥85 yrs). A total of 3,338 patients (7%) died in-hospital, and 1,794 patients (4%) died postdischarge. Among patients 16-64 yrs old, 30-day hospital mortality represented 83% of all deaths and correlation coefficients across all definitions of mortality were >.9. In patients 65-84 yrs old, 30-day hospital mortality represented 52% of all deaths, observed mortality reached expected rates at around 6 months, and agreement across mortality definitions was low. CONCLUSIONS: We observed an important variation in performance evaluation results across definitions of mortality, specifically in patients aged≥65 yrs. Half of the deaths among elders occurred later than 30 days following admission, including a significant number postdischarge. Results suggest that if performance evaluations include elderly patients, data on postdischarge mortality up to 6 months following admission are required.
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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 | 0.030 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".