Thrombolysis: Improving door-to-needle times for ischemic stroke treatment – A narrative review
Bibliographic record
Abstract
Background The effectiveness of thrombolysis is highly time dependent. For this reason, short target times have been set to reduce time to treatment from hospital arrival, which is called door-to-needle time. Summary of review There has been considerable work done at single centers and across multiple hospitals to improve door-to-needle time. There have been reductions of 8 to 47 min when applying one or more improvement strategies at single centers, and there have been many multi-hospital initiatives. The delays to treatment have been attributed to both patient and hospital factors, and strategies to address these delays have been proven to reduce door-to-needle time. The most effective strategies include pre-notification of arrival by Emergency Medical Services (EMS), single-call activation of stroke team, rapid registration process, moving the patient to computed tomography on EMS stretcher, and administration of alteplase in the scanner. There are many exciting areas of future direction including reduction of door-to-needle time in developing countries, improving pre-hospital response times, and improving the efficiency of endovascular treatment. Conclusions There is now a broad understanding of the causes of delays to fast treatment and the strategies that can be employed to improve door-to-needle time such that most centers could achieve median door-to-needle time of 30 min.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".