Impact of Weather Conditions on Neonatal Transport in Ontario: A Retrospective Cohort Study
Bibliographic record
Abstract
The successful realization of efficient neonatal transport is central to the regionalization of high-risk perinatal healthcare. Environmental factors such as weather conditions have the potential to impact transport services covering large temperate climatic zones. Our objective was to compare neonatal transport duration and relevant neonatal outcomes during winter versus summer seasons in distinct transport zones. This retrospective cohort study included newborns transported within Southwestern Ontario between January 2014 to December 2022. The serviced clinical network was divided into 4 zones based on geographical location. Transport details, patient baseline demographics, Transport Risk Index of Physiologic Stability V2 (TRIPS-II) scores, and clinically relevant outcomes were recorded. Winter (November-March) versus summer (May-September) parameters were compared within each zone. 960 transports were analyzed; 503 in summer, and 457 in winter. Baseline demographic characteristics were comparable between seasons within zones. In Zone 1, net transport time (minutes) was longer in winter versus summer (p = .019). In Zone 2, transport times were comparable; however, speed (km/min) was slower in winter versus summer (p=0.020). In Zone 3 (the Snow Belt), mean (SD) net transport times were approximately 60 minutes longer in winter versus summer [438.2(93.0) vs. 377.3(104.0), p < .001]. In Zone 4, transport times were similar between seasons. TRIPS-II scores, mortality, and major morbidity rates were comparable between seasons across all zones. This large study showed that while neonatal transport services were significantly impacted in the winter, there were no negative effects on post-transport stability, mortality, or major morbidity. Evaluation of this data might inform future service modelling.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".