Influence of geography, stroke timing, and weather conditions on transport and workflow times: Results from a longitudinal 5-year Canadian provincial registry
Bibliographic record
Abstract
BackgroundIn areas with high population spread such as Saskatchewan, it can be challenging to provide timely endovascular stroke treatment (EVT) to patients living far away from comprehensive stroke centres (CSC). We assessed the association of geography, stroke timing and weather conditions on EVT workflow times and clinical outcomes in Saskatchewan.MethodsWe included patients who underwent EVT between January 2017 and December 2022 in the province of Saskatchewan, Canada. Univariable and multivariable associations of time from last known well-to-CSC arrival, CSC arrival-to-reperfusion, and 90-day modified Rankin Score (mRS) with driving distance from patient home to CSC, transport mode, outdoor temperature and stroke timing (day & time) were assessed using descriptive statistics and multivariable regression.ResultsThree-hundred-three patients in the province of Saskatchewan underwent EVT between January 2017 and December 2022. Distance from patient home to CSC (beta-coefficient per 10 km increase = 0.02, 95% CI: 0.01-0.03) and direct to CSC transport (beta-coefficient = -0.76, 95% CI = -1.01-[-0.51]) were associated with last known well to CSC arrival time. In-hospital stroke (beta-coefficient = 0.37, 95% CI: 0.16-0.58), direct-to-CSC transfer (beta-coefficient = 0.27, 95% CI: 0.13-0.41) and daytime stroke onset (beta-coefficient = -0.15, 95% CI: -0.28-[-0.04]) were associated with time from CSC arrival to reperfusion. No association with 90-day mRS was seen.ConclusionGeographic factors and stroke timing were associated with EVT workflow times. However, no association with clinical outcomes was seen, suggesting that EVT patients living remote areas of Saskatchewan have similar benefit from EVT compared to urban areas. Every effort should be made to offer timely EVT to patients from remote areas.
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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".