Low-value injury admissions in an integrated Canadian trauma system: a multicenter cohort study
Bibliographic record
Abstract
Background: Injury represents 260,000 hospitalisations and $27 billion in healthcare costs each year in Canada. Evidence suggests that there is significant variation in the prevalence of hospital admissions among ED presentations between countries and providers but we lack data specific to injury admissions. We aimed to estimate the prevalence of potentially low-value injury admissions following injury in a Canadian provincial trauma system, identify diagnostic groups contributing most to low-value admissions and assess inter-hospital variation. Methods: We conducted a retrospective multicenter cohort study based on all injury admissions in the Québec trauma system (2013-2018). Using literature and expert consultation, we developed criteria to identify potentially low-value injury admissions. We used a multilevel logistic regression model to evaluate inter-hospital variation in the prevalence of low-value injury admissions with intraclass correlation coefficients (ICC). We stratified our analyses by age (1-15; 16-64; 65-74; 75+ years). Results: The prevalence of low-value injury admissions was 16% (n=19,163) among all patients, 26% (2136) in children, 11% (4695) in young adults and 19% (12,345) in older adults. Diagnostic groups contributing most to low-value admissions were mild traumatic brain injury in children (48% of low-value pediatric injury admissions; n=922), superficial injuries (14%, n=660) or minor spinal injuries (14%, n=634) in adults aged 16-64, and superficial injuries in adults aged 65+ (22%, n=2771). We observed strong inter-hospital variation in the prevalence of low-value injury admissions (ICC=37%). Conclusion: One out of six hospital admissions following injury may be of low-value. Children with mild traumatic brain injury and adults with superficial injuries could be good targets for future research efforts seeking to reduce health care services overuse. Inter-hospital variation indicates there may be an opportunity to reduce low-value injury admissions with appropriate interventions targeting modifications in care processes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".