Effect of Rurality on Global Access to Mechanical Thrombectomy: A Subanalysis of the MT-GLASS Study
Bibliographic record
Abstract
BACKGROUND: Mechanical thrombectomy access (MTA) for large vessel occlusion stroke varies and is limited globally. While regional studies have suggested rurality as a barrier to MTA, the magnitude and variability of this effect across countries remain unknown. This study evaluates the association of country-level rural population proportion with mechanical thrombectomy (MT) access. METHODS: We conducted an online survey of 75 countries through the Mission Thrombectomy (previously MT2020+) global professional peer network between November 22, 2020, and February 28, 2021. Surveys were distributed by regional committee chairs and completed by stroke-focused neurologists and neurointerventional physicians within the regional committees. Questions covered country-level availability of MT centers, operators, procedures, reimbursement, emergency medical services, cultural barriers, and other factors affecting stroke systems of care. MTA was defined as the estimated proportion of patients with thrombectomy-eligible large vessel occlusions receiving MT in each region annually. We used World Bank data to obtain each country’s income class based on per capita gross national income and the proportion of rural population expressed as a percentage of the total population of each country. In the final analysis, 60 countries were included. We used multivariable generalized linear models with a logit link to evaluate the association of rural population proportion with MTA. RESULTS: The median country-level rural population proportion among 60 countries was 30.7% (interquartile range, 16.3%–45.9%). In univariate generalized linear models, each 5% increase in country-level rural population proportion was associated with 22% lower odds of MTA (odds ratio, 0.78 [95% CI, 0.70–0.86]; P <0.001). After adjusting for differences in country-level health care gross domestic product, reimbursement for MT, country income class, availability of prehospital emergency medical services, training, and triage systems, each 5% increase in rural population proportion was associated with 13% lower odds of MTA (odds ratio, 0.87 [95% CI, 0.78–0.96]; P =0.006). CONCLUSIONS: Country-level rural population proportion is an independent negative predictor of access to MT. The unique challenges that rural populations experience within countries should be carefully studied to strategize and align global efforts to bridge thrombectomy access gaps and address rural-urban disparities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".