Improving Door-to-Needle Times for Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: The effectiveness of specific systems changes to reduce DTN (door-to-needle) time has not been fully evaluated. We analyzed the impact of 4 specific DTN time reduction strategies implemented prospectively in a staggered fashion. METHODS AND RESULTS: The HASTE (Hurry Acute Stroke Treatment and Evaluation) project was implemented in 3 phases at a single academic medical center. In HASTE I (June 6, 2012 to June 5, 2013), baseline performance was analyzed. In HASTE II (June 6, 2013 to January 24, 2015), 3 changes were implemented: (1) a STAT stroke protocol to prenotify the stroke team about incoming stroke patients; (2) administering alteplase at the computed tomography (CT) scanner; and (3) registering the patient as unknown to allow immediate order entry. In HASTE III (January 25, 2015 to June 29, 2015), we implemented a process to bring the patient directly to CT on the emergency medical services stretcher. Log-transformed DTN time was modeled. Data from 350 consecutive alteplase-treated patients were analyzed. Multivariable regression showed the following factors to be significant: giving alteplase in the CT (32% decrease in DTN time, 95% confidence interval [CI] 38%-55%), stretcher to CT (30% decrease in DTN time, 95% CI 16%-42%), patient registered as unknown (12% decrease in DTN time, 95% CI 3%-20%), STAT stroke protocol (11% decrease in DTN time, 95% CI 1%-20%), and stroke severity (National Institutes of Health Stroke Scale score 6-8: 19% decrease in DTN time, 95% CI 6%-31%; National Institutes of Health Stroke Scale score >8: 27% decrease in DTN time, 95% CI 17%-37%). CONCLUSIONS: Taking the patient to CT on the emergency medical services stretcher, registering the patient as unknown, STAT stroke protocol, and administering alteplase in CT are associated with lower DTN time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".