Short- and Long-Term Reduction of Door-to-Needle Time in Thrombolysis for Acute Stroke
Bibliographic record
Abstract
BACKGROUND: More timely administration of tissue plasminogen activator (alteplase) for patients with acute ischemic stroke yields greater clinical benefits. We implemented door-to-needle (DTN) time reduction strategies at our center and evaluated their short- and long-term effects on in-hospital treatment delays and clinical outcomes. METHODS: Strategies, including stroke team prenotification, direct computed tomography transfer, not routinely waiting for laboratory results and alteplase delivery on the computed tomography table, were implemented in June 2013. We included all thrombolysed patients admitted directly to our hospital between January 2012 and March 2015. In-hospital delays and symptomatic intracerebral hemorrhage rates were compared between patients pre- and postmodification, and the latter period was divided into early (first 6 months) and late (beyond 6 months) phases to assess the durability of our modifications. RESULTS: Forty-eight individuals were treated premodification compared with 58 postmodification. The median DTN time was reduced from 75 to 46 minutes (p<0.0001). The median DTN time in the early and late postmodification phases was not significantly different (41 vs 46 minutes, p=0.4085). There was no significant difference in rates of symptomatic intracerebral hemorrhage (4.2 vs 1.7%, p=0.361) or stroke mimics (2.1 ves 5.2%, p=0.625) Conclusions: We were able to decrease our DTN time for acute stroke thrombolysis by implementing relatively simple modifications and these improvements persisted over time.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".