Emergent and Urgent Transfers to Neurosurgical Centers in Ontario
Bibliographic record
Abstract
Critically ill neurosurgical patients require expedient access to neurosurgical centers (NC) to improve outcome. In regionalized health systems patients are often initially evaluated at a non-neurosurgical center (NNC) and are subsequently transferred to a NC using air or ground vehicles. We sought to identify barriers to accessing a NC for critically ill patients by analyzing interfacility transfer times and referral patterns in the province of Ontario. A retrospective observational analysis was undertaken. The cohort included patients in Ontario with emergent and urgent neurologic pathologies who underwent transfer from a NNC to NC between January 1, 2011 and December 31, 2013. Timing, clinical, and geographic data were collected for each transfer. We identified 1103 emergent/urgent transfers. The median transfer time to a NC was 3.4 h (IQR -2.2, 3.8) and varied by the geographic region of origin. A total of 17% of the patients bypassed a closer NC during transfer to their destination NC. Transfers that bypassed a closer NC travelled further (101 miles vs. 296 miles, p < 0.001), took longer (3.1 h vs. 3.9 h, p < 0.001), and in some regions were associated with a higher risk of in-transit clinical decline (3.0% vs. 8.3%, p < 0.05) when compared with transfers that ended at the closest NC. Regionalization of neurosurgical services in Ontario has led to heavy reliance upon patient transfers to maintain continuity of care. Access to a NC varied across the province, which may represent regional differences in neurosurgical bed availability, resource limitations at smaller NCs, or environmental factors. Our descriptions of referral patterns and transport times can guide health system planning in Ontario and similar jurisdictions in the United States and Canada.
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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) | 0.000 | 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".