Bibliographic record
Abstract
Introduction: Given the mortality benefit related to trauma centre care among severely injured patients, multiple initiatives have been undertaken to minimize the admission of these patients to non-trauma centres (undertriage). An unintended consequence of these initiatives is the transfer of patients without severe injuries to trauma centres (overtriage). To better understand the impact of overtriage on the healthcare system, this thesis aimed to evaluate hospital characteristics associated with, and quantify healthcare costs attributable to, overtriage. Methods: We identify all injured adults who presented to a non-trauma centre in Ontario using population-based administrative data (2009–2020). Bivariate generalized mixed effects models were used to estimate the accuracy of triage decisions for patients transferred from non-trauma to trauma centres. Rates of under- and overtriage were compared between centres with high and low triage accuracy using negative binomial regression. Hierarchical logistic regression was then used to investigate the impact of hospital resources on overtriage, adjusting for patient and injury characteristics. Finally, we performed a propensity-matched cohort analysis to estimate differences in 30-day healthcare costs between overtriaged patients and matched controls. Results: Triage accuracy varied significantly across non-trauma centres (diagnostic odds ratios ranging from 0.20 to 21.1). After adjusting for case-mix and resources, rates of overtriage were 75% lower at highly accurate centres relative to those with low accuracy (RR 0.27, 95% CI: 0.20–0.36), whereas there were no differences in undertriage (RR 1.06, 95% CI: 1.00–1.12). Resources at the hospital of presentation, including CT scanners, surgical support, and intensive care units did not impact a patient’s likelihood of overtriage. However, hospital of presentation itself had a greater impact on a patient’s odd of overtriage than any patient, injury, or hospital characteristic (MOR 3.76). Thirty-day healthcare costs were 6.5% greater among overtriaged patients relative to matched controls (RR 1.06, 95% CI: 1.04–1.09). Conclusion: Overtriage represents a key contributor to the accuracy of trauma transfers. Our findings suggest there are high performing centres that have developed processes to ensure patients without severe injuries are cared for locally. Dissemination of these processes represents an opportunity to improve trauma systems and potentially reduce healthcare spending.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".