Canadian benchmarks for acute injury care
Bibliographic record
Abstract
Background: Acute care injury outcomes vary substantially across Canadian provinces and trauma centres. Our aim was to develop Canadian benchmarks to monitor mortality and hospital length of stay (LOS) for injury admissions. Methods: Benchmarks were derived using data from the Canadian National Trauma Registry on patients with major trauma admitted to any level I or II trauma centre in Canada and from the following patient subgroups: isolated traumatic brain injury (TBI), isolated thoracoabdominal injury, multisystem blunt injury, age 65 years or older. We assessed predictive validity using measures of discrimination and calibration, and performed sensitivity analyses to assess the impact of replacing analytically complex methods (multiple imputation, shrinkage estimates and flexible modelling) with simple models that can be implemented locally. Results: The mortality risk adjustment model had excellent discrimination and calibration (area under the receiver operating characteristic curve 0.886, Hosmer–Lemeshow 36). The LOS risk-adjustment model predicted 29% of the variation in LOS. Overall, observed:expected ratios of mortality and mean LOS generated by an analytically simple model correlated strongly with those generated by analytically complex models (r > 0.95, κ on outliers > 0.90). Conclusion: We propose Canadian benchmarks that can be used to monitor quality of care in Canadian trauma centres using Excel (see the appendices, available at canjsurg.ca). The program can be implemented using local trauma registries, providing that at least 100 patients are available for analysis.
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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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".