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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".