Neurosurgery (General Neurosurgery)
Bibliographic record
Abstract
Background: Critically ill neurosurgical patients require expedient access to neurosurgical centers (NC) to improve outcome. In Ontario, many patients are initially evaluated at a non-neurosurgical center (NNC) and subsequently transferred to a NC by a provincial service using air or ground vehicles. We characterized transfers from NNC to NC for critically ill patients. Methods: 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. Results: We identified 1103 emergent/urgent transfers. The mean transfer time to a NC was 3.4hrs (SD – 3.0) and varied by the geographic region of origin. 17% of patients bypassed a closer NC during transfer to their destination NC. Transfers that bypassed a closer NC travelled further (162km vs. 477km, p<0.001), took longer (3.1hrs vs. 3.9hrs, 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. Conclusions: Transport time to a NC varied across Ontario. Transfers occasionally bypassed the nearest NC, which may reflect neurosurgical bed availability, resource limitations, or patient needs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.166 | 0.041 |
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".