The relative importance of clinical factors in initiating interfacility transfer of major trauma patients: A discrete choice experiment
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Bibliographic record
Abstract
Introduction and Objectives Approximately 30% of patients meeting severe injury criteria are never transferred to lead trauma centers (LTCs). The reasons for this gap are not fully understood but involve both system-level factors and individual decision-making. We used a method called discrete choice modeling (DCM) to evaluate which clinical and demographic patient factors might make emergency physicians more likely to initiate transfers to LTCs. Methods An email survey was distributed to physicians working in emergency departments (EDs) in Ontario. The relative importance of clinical and demographic patient attributes as drivers for transfer was evaluated using DCM. Simulated patient cases were created using a random generator to combine attributes. Each respondent was presented with 36 different patients in sets of three and asked if they would transfer each patient to an LTC. The relative importance of each driver was then compared across physician characteristics. Results One hundred and fifty three emergency physicians completed the survey. The drivers for transfer, expressed as utility scores, were derangements in hemodynamics (22), CNS/head injuries (19), pelvic fractures (11), chest injuries (10), comorbidities (9), abdominal injuries (8), extremity injuries (7), mechanism of injury (7), age (5), and gender (2). Drivers for patient transfer did not differ based on physician experience or type of training. Conclusion In this DCM study, the clinical and demographic factors most likely to make emergency physicians consider patient transfers to LTCs were patient hemodynamic derangements and CNS/head injuries. Overall, these drivers did not differ by physician experience or training. An understanding of such patient-level drivers for transfers to LTCs may improve the implementation of evidence-based interfacility transfer criteria.
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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) | 0.001 | 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 it