Association of Neighborhood Deprivation With Thrombolysis and Thrombectomy for Acute Stroke in a Health System With Universal Access
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The association between socioeconomic status and acute ischemic stroke treatments remain uncertain, particularly in countries with universal health care systems. This study aimed to investigate the association between neighborhood-level material deprivation and the odds of receiving IV thrombolysis or thrombectomy for acute ischemic stroke within a single-payer, government-funded health care system. METHODS: We conducted a population-based cohort study using linked administrative data from Ontario, Canada. This study involved all community-dwelling adult Ontario residents hospitalized with acute ischemic stroke between 2017 and 2022. Neighborhood-level material deprivation, measured in quintiles from least to most deprived, was our main exposure. We considered the receipt of thrombolysis or thrombectomy as the primary outcome. We used multivariable logistic regression models adjusted for baseline differences to estimate the association between material deprivation and outcomes. We performed a sensitivity analysis by additionally adjusting for hospital type at initial assessment. Furthermore, we tested whether hospital type modified the associations between deprivation and outcomes. RESULTS: interaction >0.1). DISCUSSION: We observed disparities in the use of thrombolysis or thrombectomy for acute ischemic stroke by socioeconomic status despite access to universal health care. Targeted health care policies, public health messaging, and resource allocation are needed to ensure equitable access to acute stroke treatments for all patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".