The impact of referring hospital resources on interfacility overtriage: A population-based analysis
Bibliographic record
Abstract
BACKGROUND: Nearly half of patients transferred from non-trauma to trauma centres have minor injuries. The transfer of patients with minor injuries to trauma centres is not associated with any known patient benefit and represents an opportunity to reduce healthcare costs and improve patient experience. In this study, we evaluated the relationship between hospital resources and overtriage, with the objective of identifying targets for system-level intervention. METHODS: We conducted a population-based cohort study of adults, age ≥ 16, presenting with minor injuries to non-trauma centres in Ontario, Canada (2009-2020). The primary outcome was overtriage, defined as transfer to a trauma centre. Hierarchical logistic regression was used to evaluate the association between hospital resources and a patient's likelihood of being overtriaged, adjusting for case-mix. RESULTS: amongst 165,302 patients with minor injuries, 15,641 (9.5 %) were transferred to a trauma centre (overtriage). Presence of a CT scanner, surgical support, or intensive care unit had no impact on a patient's likelihood of overtriage. Relative to community hospitals, presentation to a teaching hospital was independently associated with greater odds of overtriage (OR 2.97, 95 % CI: 1.26-7.00). Accounting for case-mix and resources, the median difference in a patient's odds of overtriage varied 3.7-fold across non-trauma centres (MOR 3.76). CONCLUSIONS: There is significant variability in overtriage across non-trauma centres, even after adjusting for case-mix and hospital resources. These finding suggests that some centres have developed processes to minimize overtriage independent of available resources. Broad implementation of these processes may represent an opportunity for system-wide quality improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".