Influence of Socioeconomic Status on Trauma Center Performance Evaluations in a Canadian Trauma System
Bibliographic record
Abstract
BACKGROUND: Trauma center performance evaluations generally include adjustment for injury severity, age, and comorbidity. However, disparities across trauma centers may be due to other differences in source populations that are not accounted for, such as socioeconomic status (SES). We aimed to evaluate whether SES influences trauma center performance evaluations in an inclusive trauma system with universal access to health care. STUDY DESIGN: The study was based on data collected between 1999 and 2006 in a Canadian trauma system. Patient SES was quantified using an ecologic index of social and material deprivation. Performance evaluations were based on mortality adjusted using the Trauma Risk Adjustment Model. Agreement between performance results with and without additional adjustment for SES was evaluated with correlation coefficients. RESULTS: The study sample comprised a total of 71,784 patients from 48 trauma centers, including 3,828 deaths within 30 days (4.5%) and 5,549 deaths within 6 months (7.7%). The proportion of patients in the highest quintile of social and material deprivation varied from 3% to 43% and from 11% to 90% across hospitals, respectively. The correlation between performance results with or without adjustment for SES was almost perfect (r = 0.997; 95% CI 0.995-0.998) and the same hospital outliers were identified. CONCLUSIONS: We observed an important variation in SES across trauma centers but no change in risk-adjusted mortality estimates when SES was added to adjustment models. Results suggest that after adjustment for injury severity, age, comorbidity, and transfer status, disparities in SES across trauma center source populations do not influence trauma center performance evaluations in a system offering universal health coverage.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".