Individual and County‐Level Social Determinants of Health and Acute Reperfusion Therapies: Get With The Guidelines‐Stroke Registry Results
Bibliographic record
Abstract
BACKGROUND: The associations between individual- and county-level social determinants of health and reperfusion therapies (thrombolysis or thrombectomy) for acute ischemic stroke have been described separately, but they are rarely studied together. METHODS AND RESULTS: We identified 1.5 million patients aged ≥40 years with acute ischemic stroke between January 1, 2015 and December 31, 2019 from the Get With The Guidelines-Stroke registry in the United States. We ascertained age, sex, rural residence, and ethnicity or race at the individual level, and poverty, unemployment, and lower education (defined as less than high school) at the county level. We used multivariable log-binomial regression models estimated using generalized estimating equations methods to account for county-level clustering and adjusted for comorbidities. About 13.4% (n=203 800) patients received reperfusion therapy. Black (adjusted risk ratio [aRR, 1.06 [95% CI, 1.04-1.07]) and Hispanic (aRR, 1.36 [95% CI, 1.33-1.40]) patients were more likely to receive it compared with White patients, as were those in counties with lower education (aRR, 1.08 [95% CI, 1.07-1.09]). Older adults (5-year increase in age aRR, 97 [95% CI, 0.97-0.97]), rural residents (aRR, 0.58 [95% CI, 0.56-0.59]), and those with missing last known well time (aRR, 0.30 [95% CI, 0.29-0.30]) were less likely to receive it. Missing last known well was less likely in Hispanic (aRR, 0.94 [95% CI, 0.92-0.95]) and Asian (aRR, 0.93 [95% CI, 0.90-0.96]) patients compared with White patients and more likely in those residing in counties with high unemployment (aRR, 1.07 [95% CI, 1.06-1.08]). CONCLUSIONS: Individual- and county-level social determinants of health were associated with reperfusion therapies and missing last known well times. Understanding the mechanisms by which these factors could affect treatment eligibility through time-based criteria can help increase reperfusion therapies for all.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".