Strategies Used by Hospitals to Improve Speed of Tissue-Type Plasminogen Activator Treatment in Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The benefits of intravenous tissue-type plasminogen activator 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 using these strategies and their association with DTN times have not been well studied. METHODS: We surveyed 304 Get With The Guidelines-Stroke hospitals joining TARGET: Stroke regarding their baseline use of strategies to reduce DTN times in the January 2008 to December 2009 time frame before the initiation of TARGET: Stroke and determined the association between hospital strategies and DTN times. RESULTS: Among 5460 patients receiving tissue-type plasminogen activator within 3 hours of symptom onset in surveyed hospitals, the median DTN time was 72 minutes (interquartile range, 55-94). Reported use of the different strategies varied considerably. Of 11 hospital strategies analyzed individually by multivariable analysis, 3 strategies were independently associated with shorter DTN times. These included rapid triage/stroke team notification (209/304 [69%] hospitals, 8.1-minute reduction in DTN time), single-call activation system (190/304 [63%] hospitals, 4.3 minutes), and tissue-type plasminogen activator stored in the emergency department (189/304 [62%] hospitals, 3.5 minutes). When analyzed incrementally, hospitals that used a greater number of strategies had shorter DTN times with 1.3 minutes (adjusted mean difference) saved for each strategy implemented (14 minutes if all strategies were used). CONCLUSIONS: Although the majority of participating hospitals reported using some strategy to reduce delays in tissue-type plasminogen activator administration for acute ischemic stroke, the strategies applied vary considerably and those most strongly associated with shorter DTN times were applied relatively less frequently.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".