Abstract 206: Pre-notification by Emergency Medical Services is Associated with More Timely Evaluation and Treatment of Acute Ischemic Stroke
Bibliographic record
Abstract
Background: The benefits of intravenous tissue-plasminogen activator (tPA) in acute ischemic stroke are time-dependent. Emergency medical services (EMS) pre-notification of stroke arrivals may provide a means of reducing evaluation and treatment times. In this study we used data from the nationwide Get With The Guidelines Stroke (GWTG-Stroke) program to determine the effect of EMS pre-notification on acute ischemic stroke processes of care. Methods: Acute ischemic stroke patients transported by EMS to 1585 GWTG-Stroke hospitals from April 2003 to March 2011 were studied. The association between EMS pre-notification and door-to-imaging (DTI) times, door-to-needle (DTN) times, onset-to-needle times (OTN), and tPA treatment rates were analyzed using multivariable GEE regression analyses. Results: Of 371,988 EMS transported acute ischemic stroke patients, EMS pre-notification occurred in 249,197 (67.0%). Patients with pre-notification had shorter door-to-imaging times, shorter onset-to-needle times, and were more likely to be treated with tPA when eligible ( Table ). EMS pre-notification was independently associated with increased odds of DTI ≤25 minutes (adjusted OR 1.53, 95% CI 1.44–1.63, p<0.0001), DTN times ≤60 minutes (aOR 1.20, 95% CI 1.10–1.31, p<0.0001), OTN times (aOR 1.17, 95% CI 1.09–1.25, p<0.0001), and tPA use within 3 hours among eligible patients arriving by 2 hours (aOR 1.64, 95% CI 1.50–1.79, p<0.0001), without significant increases in complications of thrombolytic therapy. Conclusion: EMS pre-notification is independently associated with more rapid patient imaging and increased timeliness in IV tPA administration. These results support the need for initiatives targeted at increasing EMS pre-notification rates as a mechanism from improving quality of care and outcomes in acute ischemic stroke.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".