Abstract WP280: Use of Strategies to Improve Door-to-Needle Times with Tissue Plasminogen Activator in Acute Ischemic Stroke by US Hospitals: Findings from the Target: Stroke Survey
Bibliographic record
Abstract
Background: The benefits of intravenous tissue-plasminogen activator (tPA) in acute ischemic stroke are time-dependent and several strategies have been reported to be associated with more rapid door-to-needle (DTN) times. However, the extent to which hospitals are utilizing these strategies has not been well studied. Methods: We surveyed 304 hospitals joining Target: Stroke regarding their baseline use of strategies to reduce door-to-needle times in the 1/2008-2/2010 timeframe (prior to the initiation of Target: Stroke). The survey was developed based on literature review and expert consensus for strategies identified as being associated with shorter DTN times and further refined after pilot testing. Categorical responses are reported as frequencies. Results: Hospitals participating in the survey were 50% academic, median 163 (IQR 106-247) ischemic stroke admissions per year, median 10 (IQR 6-17) tPA treated patients per year, and had median 79 minute (IQR 71-89) DTN times. By survey, 214 of 304 hospitals (70%) reported initiating or revising strategies to reduce DTN times in the prior 2 years. Reported use of the different strategies varied in frequency, with use of ischemic stroke critical pathways, CT scanner located in the Emergency Department, and tPA being stored in the Emergency Department being the strategies least frequently employed (Table). As part of Target: Stroke participation, 279 of 304 hospitals (91.5%) indicated they planned to have a dedicated team focused on reducing DTN times. Conclusions: While most US hospitals participating in this survey report use of the strategies to improve the timeliness of tPA administration for acute ischemic stroke, significant variation exists. Further research is needed to understand which of these strategies are most effective in improving acute ischemic 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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".