Improving Door-to-Needle Times in Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The benefits of intravenous tissue-type plasminogen activator (tPA) in acute ischemic stroke are time-dependent, and guidelines recommend a door-to-needle time of ≤60 minutes. However, fewer than one third of acute ischemic stroke patients who receive tPA are treated within guideline-recommended door-to-needle times. This article describes the design and rationale of TARGET: Stroke, a national initiative organized by the American Heart Association/American Stroke Association in partnership with other organizations to assist hospitals in increasing the proportion of tPA-treated patients who achieve guideline-recommended door-to-needle times. METHODS: The initial program goal is to achieve a door-to-needle time≤60 minutes for at least 50% of acute ischemic stroke patients. Key best practice strategies previously associated with achieving faster door-to-needle times in acute ischemic stroke were identified. RESULTS: The 10 key strategies chosen by TARGET: Stroke include emergency medical service prenotification, activating the stroke team with a single call, rapid acquisition and interpretation of brain imaging, use of specific protocols and tools, premixing tPA, a team-based approach, and rapid data feedback. The program includes many approaches intended to promote hospital participation, implement effective strategies, share best practices, foster collaboration, and achieve stated goals. A detailed program evaluation is also included. In the first year, TARGET: Stroke has enrolled over 1200 United States hospitals. TARGET: Stroke, a multidimensional initiative to improve the timeliness of tPA administration, aims to elevate clinical performance in the care of acute ischemic stroke, facilitate the more rapid integration of evidence into clinical practice, and improve outcomes.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".