Abstract 165: Age Related Variations in Improvement in Door-to-Needle Times in Acute Ischemic Stroke: Findings From Target: Stroke Phase I
Bibliographic record
Abstract
Background: While shorter door-to-needle (DTN) times with intravenous tPA is associated with better outcomes, prior studies have demonstrated that older patients are less likely to be treated within 60 minutes. Whether stroke quality improvement programs can impact care to a similar degree for older and younger patients has not been well studied. This study aims to assess the improvements in DTN times before and after the launch of Target: Stroke Phase I in 2010 among different age groups. 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 patients age <60, 60-69, 70-79, ≥80 years. Data were adjusted for patient and hospital characteristics, including stroke severity. Results: There were 71,169 intravenous tPA treated patients (27,303 pre-; 43,866 post-Target Stroke) from 1030 GWTG-Stroke participating hospitals. Patients were median age 72 (IRQ 60-82). 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. Patients ≥80 years were less likely to have DTN times ≤ 60 minutes pre-TS. There were slightly greater cumulative improvements in DTN times among the older age groups after adjustment for other patient and hospital characteristics (Table). While the differences in DTN ≤ 60 minutes were narrowed, they were not eliminated. Conclusions: The implementation of Target: Stroke was associated with significant improvements in DTN times across all age groups, but differences by age, while narrowed, still persisted post-Target: Stroke. Despite overall progress, additional efforts will be needed to address these age related differences in timely stroke care.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".