D132. Improving Patient Transfer Quality with a Dedicated Transfer Center in an Emergency Setting: The Example of a Digital Replantation Program
Bibliographic record
Abstract
PURPOSE: Centralization to trauma centers is the gold standard for digital revascularization, but avoidable transfers constitute a real challenge for the trauma network. Our transfer center is characterized by a 24/7 call center managed by specialized nurses responsible for systematic data collection, pictures and imagery transmissions and arrangement of systematic recorded discussions between referring doctors and specialized surgeons in revascularization. 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 2017 to December 2021. We included all transfer requests from outside facilities to revascularize upper extremity injuries. Transfers were considered avoidable if no microsurgical procedure was attempted or completed. Univariate and multivariate analysis were employed to compare transfer outcomes before and after the addition of the transfer center in 2019. RESULTS: 795 transfer requests were analyzed, of which 326 occurred before the implementation of the transfer center and 469 occurred after. Following this addition, the incidence of transfer requests increased from 13,48 requests/month to 16.96 (p=0.016). Avoidable transfers significantly decreased following the implementation of the standardized triage protocol by the call center. 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). CONCLUSION: 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".