Geographic Access to Stroke Care Services in Rural Communities in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Optimal stroke care requires access to resources such as neuroimaging, acute revascularization, rehabilitation, and stroke prevention services, which may not be available in rural areas. We aimed to determine geographic access to stroke care for residents of rural communities in the province of Ontario, Canada. METHODS: We used the Ontario Road Network File database linked with the 2016 Ontario Acute Stroke Care Resource Inventory to estimate the proportion of people in rural communities, defined as those with a population size <10,000, who were within 30, 60, and 240 minutes of travel time by car from stroke care services, including brain imaging, thrombolysis treatment centers, stroke units, stroke prevention clinics, inpatient rehabilitation facilities, and endovascular treatment centers. RESULTS: Of the 1,496,262 people residing in rural communities, the majority resided within 60 minutes of driving time to a center with computed tomography (85%), thrombolysis (81%), a stroke unit (68%), a stroke prevention clinic (74%), or inpatient rehabilitation (77.0%), but a much lower proportion (32%) were within 60 minutes of driving time to a center capable of providing endovascular thrombectomy (EVT). CONCLUSIONS: Most rural Ontario residents have appropriate geographic access to stroke services, with the exception of EVT. This information may be useful for jurisdictions seeking to optimize the regional organization of stroke care services.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".