The Impact of Precipitation on Land Interfacility Transport Times
Bibliographic record
Abstract
INTRODUCTION: Timely transfer of patients among facilities within a regionalized critical-care system remains a large obstacle to effective patient care. For medical transport systems where dispatchers are responsible for planning these interfacility transfers, accurate estimates of interfacility transfer times play a large role in planning and resource-allocation decisions. However, the impact of adverse weather conditions on transfer times is not well understood. HYPOTHESIS/PROBLEM: Precipitation negatively impacts driving conditions and can decrease free-flow speeds and increase travel times. The objective of this research was to quantify and model the effects of different precipitation types on land travel times for interfacility patient transfers. It was hypothesized that the effects of precipitation would accumulate as the distance of the transfer increased, and they would differ based on the type of precipitation. METHODS: Urgent and emergent interfacility transfers carried out by the medical transport system in Ontario from 2005 through 2011 were linked to Environment Canada's (Gatineau, Quebec, Canada) climate data. Two linear models were built to estimate travel times based on precipitation type and driving distance: one for transfers between cities (intercity) and another for transfers within a city (intracity). RESULTS: Precipitation affected both transfer types. For intercity transfers, the magnitude of the delays increased as driving distance increased. For median-distance intercity transfers (48 km), snow produced delays of approximately 9.1% (3.1 minutes), while rain produced delays of 8.4% (2.9 minutes). For intracity transfers, the magnitude of delays attributed to precipitation did not depend on distance driven. Transfers in rain were 8.6% longer (1.7 minutes) compared to no precipitation, whereas only statistically marginal effects were observed for snow. CONCLUSION: Precipitation increases the duration of interfacility land ambulance travel times by eight percent to ten percent. For transfers between cities, snow is associated with the longest delays (versus rain), but for transfers within a single city, rain is associated with the longest delays.
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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) | 0.000 | 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".