Impact of Weather Conditions on Neonatal Transport in Ontario: A Retrospective Cohort Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it