The influence of socioeconomic disparities across source populations on the results of trauma centre performance evaluations in a Canadian trauma system
Bibliographic record
Abstract
The evaluation of acute trauma care is essential to the effort of alleviating the societal burden of injury. Trauma centre performance evaluations generally include adjustment for anatomic injury severity, physiological reaction to injury, physiological reserve and transfer status. However, socioeconomic status (SES) has been shown to be related to health outcomes and disparities across trauma centre source populations may bias performance evaluations. We aimed to evaluate whether SES influences risk-adjusted mortality following trauma in an inclusive trauma system with free access to medical care. The study was based on patients treated for major trauma in the inclusive trauma system of the province of Quebec, Canada (1999–2006). SES was quantified using an ecological index of material and social deprivation via patients residential postal code. Hierarchical logistic regression was used to evaluate the independent influence of SES on hospital mortality. The study sample comprised a total of 88 235 patients from 59 trauma centres, including 4731 deaths (5.4%). The proportion of patients in the highest quintile of material and social deprivation varied from 11% to 90% and from 3% to 43% across hospitals, respectively. After adjusting for anatomic injury severity, physiological reaction to injury, physiological reserve and transfer status, neither material (OR 0.97, 95% CI 0.94 to 1.01) nor social deprivation (OR 1.02, 95% CI 0.99 to 1.06) were associated with hospital mortality. This study suggests that in an inclusive trauma system with free access to healthcare, disparities in SES across source populations should not lead to biased trauma centre mortality evaluations, providing an adequate risk adjustment strategy is used.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".