Access to Endovascular Thrombectomy: Does Driving Time to Comprehensive Stroke Center Matter More Than Rurality?
Bibliographic record
Abstract
BACKGROUND: Acute stroke treatments are highly time-sensitive, with geographical disparities affecting access to care. This study examined the impact of driving distance to the nearest comprehensive stroke center (CSC) and rurality on the use of thrombectomy or thrombolysis in Ontario, Canada. METHODS: This retrospective cohort study used administrative data to identify adults hospitalized with acute ischemic stroke between 2017 and 2022. Driving time from patients' residences to the nearest CSC was calculated using the Ontario Road Network File and postal codes. Rurality was categorized using postal codes. Multivariable logistic regression, adjusted for baseline differences, estimated the association between driving distance and treatment with thrombectomy (primary outcome) or thrombolysis (secondary outcome). Driving time was modeled as a continuous variable using restricted cubic splines. RESULTS: Data from 57,678 patients (median age 74 years, IQR 64-83) were analyzed. Increased driving time was negatively associated with thrombectomy in a nonlinear fashion. Patients living 120 minutes from a CSC were 20% less likely to receive thrombectomy (adjusted odds ratio [aOR] 0.80, 95% CI 0.62-1.04), and those 240 minutes away were 60% less likely (aOR 0.41, 95% CI 0.28-0.60). Driving time did not affect thrombolysis rates, even at 240 minutes (aOR 1.0, 95% CI 0.70-1.42). Thrombectomy use was similar in medium urban areas (aOR 0.80, 95% CI 0.56-1.16) and small towns (aOR 0.78, 95% CI 0.57-1.06) compared to large urban areas. CONCLUSION: Thrombolysis access is equitable across Ontario, but thrombectomy access decreases with increased driving distance to CSCs. A multifaceted approach, combining healthcare policy innovation and infrastructure development, is necessary for equitable thrombectomy delivery.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".