Risk Factors for Non-optimal Resource Utilization for Emergent Interfacility Transfers by Air Ambulance in Ontario
Bibliographic record
Abstract
Background: The use of air ambulance to facilitate interfacility transfer has been associated with improved mortality; however, air ambulance is a limited resource and sometimes the optimal resource to transport a patient is unavailable. When a non-optimal resource is used there is an inherent delay and critically unwell patients may deteriorate as a result. This study aimed to identify risk factors associated with non-optimal resource utilization for adult patients undergoing emergent interfacility transport by air ambulance in Ontario, Canada. A secondary objective was to determine if non-optimal resource utilization was associated with deterioration in clinical status by measuring a delta rapid emergency medicine score (REMS). Methods: This was a retrospective cohort study of all emergent, adult interfacility transfers transported by air ambulance over a 5-year period in Ontario, Canada. Determination of optimal resource use was based on distances and historic time data for all sending-receiving facility pairs. A logistic regression model was used to explore patient, provider and institutional risk factors for non-optimal resource use. To explore the secondary objective a linear regression model was used to explore impact of non-optimal resource use on deltaREMS. Results: There were a total of 9,687 patients included in the study cohort, with 4,984 having an optimal resource use and 4,703 having non-optimal resource. The median delay in interfacility transfer caused by a non-optimal transfer strategy was 35.7 minutes. Patients who required mechanical ventilation (OR 1.13, p = 0.031) and or were transferred out of nursing stations had higher odds of non-optimal resource use (OR 2.84, p = 0.019). Paramedic level of care of advanced (OR 0.37, p = < 0.001) and critical care (OR 0.28, p = < 0.001) as well as spring season (OR 0.75, p = < 0.001) had lower odds of non-optimal resource utilization. Optimal resource utilization did not significantly affect delta REMS (beta coefficient 0.002, p = 0.64). Conclusions: Patients who required mechanical ventilation and were transferred out from a nursing station had higher odds of non-optimal resource utilization while patients that required advanced or critical care level of care and spring season had lower odds of non-optimal resource use. Additionally, non-optimal resource use for air ambulance interfacility transfers did not result in patient deterioration as measured by a delta REMS score.
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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 | 0.000 |
| 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) | 0.001 | 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".