Abstract 114: Equitable Improvements in Sex and Race/Ethnic Specific Door-to-Needle Times in Acute Ischemic Stroke: Findings From Target: Stroke Phase I
Bibliographic record
Abstract
Background: Prior studies have demonstrated that women and black patients with acute ischemic stroke were significantly less likely to be treated with intravenous tPA with door-to-needle (DTN) times of ≤60 minutes. Whether stroke quality improvement programs can impact care to a similar degree among sex and race/ethnic groups has not been well studied. This study aims to assess sex and race/ethnicity specific improvements in DTN times before and after the launch of Target: Stroke Phase I in 2010. Methods: Target: Stroke identified and disseminated 10 best practice strategies, provided clinical decision support tools, and set hospital recognition goals. Rates of DTN times ≤60 minutes and cumulative improvements pre- 2003-2009 were compared to post-Target Stroke 2010-2013 for sex and race/ethnic groups. Data were adjusted for patient and hospital characteristics. Results: There were 71,169 intravenous tPA treated patients (27,303 pre-; 43,866 post-Target Stroke) from 1030 GWTG-Stroke hospitals. Patients were median age 72, 50.1% women, and 72.0% white, 13.8% black, and 6.6% Hispanic. Overall, patients with DTN times ≤ 60 minutes increased from 26.5% (95% CI 26.0-27.1%) pre-intervention to 41.3% (95% CI 40.8-41.7%) post-intervention (P<.0001), reaching 51.0% in 2013. There were no significant differences in cumulative improvements in DTN times by sex and race/ethnic groups, even after adjustment for other patient and hospital characteristics (Table). Conclusions: The implementation of Target: Stroke was associated with equitable improvements in DTN times for men and women and for black, white, and Hispanic ischemic stroke patients. These findings highlight the role that quality improvement programs can play in providing more timely and effective stroke care for all patients, irrespective of sex and 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.005 | 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".