Abstract 43: Race-Ethnic Specific Trends in Stroke Thrombolysis Care Metrics in Relation to U.S. Target: Stroke Nationwide Quality Improvement Program 2003-2021
Bibliographic record
Abstract
Objectives: To examine whether thrombolysis care metrics have improved in all races and ethnicities with the launch and advance of Target: Stroke (TS) national quality initiatives. Methods: This cohort study included patients presenting to Get With The Guidelines (GWTG)-Stroke participating hospitals within 4.5 hours of ischemic stroke onset from 2003 to 2021. Thrombolysis rates and speed of treatment during TS phase I (2010-2013), II (2014-2018), and III (2019-2021) were compared with the pre-TS period (2003-2009). Results: Among all the patients with ischemic stroke, Asian, Black, and Hispanic individuals, compared with White, were significantly more likely to present after the 4.5-hour thrombolysis treatment window (Figure/Panel A and B). Among the 1,182,182 patients arriving within 4.5 hours, 33,375 were Asian, 180,315 Black, 86,831 Hispanic, and 881,661 White. Unadjusted rates of thrombolysis treatment increased in all race-ethnic groups from 2003 to 2021 (Figure/Panel C) with no significant disparities. Disparities were evident in adjusted analyses (Figure/Panel D) and persisted in TS:III when, compared with White patients, Asian, Black, and Hispanic patients had significantly lower odds of receiving thrombolysis (0.85, 0.76, and 0.86) (all p values<0.05). Door-to-needle (DTN) times significantly improved in all race-ethnic groups during TS (Figure/Panel E). However, compared with White patients, Asian, Black, and Hispanic patients had significantly lower odds of DTN≤60 minutes (Figure/Panel F) which persisted in TS:III (adjusted OR 0.91, 0.78, and 0.87) (all p values<0.05). Conclusion: Target: Stroke was associated with substantial improvement in thrombolysis frequency and timeliness for all races and ethnicities, but disparities persisted after risk adjustment. Further stroke system of care and health equity interventions should focus on early hospital arrival and quality reporting of risk-adjusted care metrics by race-ethnicity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".