Improving Patient Transfer in a Canadian Upper Extremity Revascularization Program
Bibliographic record
Abstract
BACKGROUND: Centralization to trauma centers is the standard for digital revascularization, but avoidable transfers constitute a real challenge to the trauma network. The authors' transfer center is characterized by a 24-hour/7-day call center managed by specialized nurses responsible for data collection, pictures, and imagery transmissions and arrangement of systematic recorded discussions between referring doctors and surgeons. This study investigated the impact of implementing a dedicated transfer center on avoidable patient transfers to a centralized quaternary care center for upper extremity injuries requiring revascularization. METHODS: A retrospective study was performed from September of 2017 to December of 2021. The authors included all transfer requests from outside facilities to revascularize upper extremity injuries. Transfers were considered avoidable if no exploration or microsurgical vascular procedure was attempted. Univariate and multivariate analyses were used to compare transfer outcomes before and after the addition of the transfer center in 2019. RESULTS: A total of 795 transfer requests were analyzed, of which 326 occurred before the implementation of the transfer center and 469 occurred afterward. Following this addition, the incidence of transfer requests increased from 13.48 requests per month to 16.96 (P = 0.016). It improved communications between referring doctors and surgeons and improved triage selection, resulting in an increase of transfer refusals (from 47% to 56%; P = 0.009). Avoidable transfers decreased significantly by 7.32% (P = 0.047) following the implementation of the standardized protocol and call center. CONCLUSIONS: Unnecessary interhospital transfers in the context of digital revascularization burden the trauma network. Introducing a dedicated transfer center reduces avoidable transfers by improving triage selection and communications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".