Temporal Trends in Racial and Ethnic Disparities in Endovascular Therapy in Acute Ischemic Stroke
Bibliographic record
Abstract
Introduction Endovascular therapy (EVT) use increased following clinical trials publication in 2015, but limited data suggest there may be persistent race and ethnicity differences. Methods and Results We included all patients with acute ischemic stroke arriving within 6 hours of last known well and with National Institute of Health Stroke Scale (NIHSS) score ≥6 between April 2012 and June 2019 in the Get With The Guidelines-Stroke database and evaluated the association between race and ethnicity and EVT use and outcomes, comparing the era before versus after 2015. Of 302 965 potentially eligible patients; 42 422 (14%) underwent EVT. Although EVT use increased over time in all racial and ethnic groups, Black patients had reduced odds of EVT use compared with non-Hispanic White (NHW) patients (adjusted odds ratio [aOR] before 2015, 0.68 [0.58‒0.78]; aOR after 2015, 0.83 [0.76‒0.90]). In-hospital mortality/discharge to hospice was less frequent in Black, Hispanic, and Asian patients compared with NHW. Conversely discharge home was more frequent in Hispanic (29.7%; aOR, 1.28 [1.16‒1.42]), Asian (28.2%; aOR, 1.23 [1.05‒1.44]), and Black (29.1%; aOR, 1.08 [1.00‒1.18]) patients compared with NHW (24%). However, at 3 months, functional independence (modified Rankin Scale, 0-2) occurred less frequently in Black (37.5%; aOR, 0.84 [0.75‒0.95]) and Asian (33%; aOR, 0.79 [0.65‒0.98]) patients compared with NHW patients (38.1%). Conclusions In a large cohort of patients treated with EVT, Black versus NHW patient disparities in EVT use have narrowed over time but still exist. Discharge related outcomes were slightly more favorable in racial and ethnic underrepresented groups; 3-month functional outcomes were worse but improved across all groups with time.
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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 | 0.006 |
| 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) | 0.003 | 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".