Abstract TP394: The 3- R’s of a Stroke Program: Recognize Staff, Raise Alteplase Administration, Reduce Door-to-Needle times
Bibliographic record
Abstract
Background: Early recognition of an acute stroke with timely administration of Alteplase has been shown to reduce disability by as much as 30% at 90-days. The American Heart Association/American Stroke Association endorses Alteplase be administered as quickly as possible. Emergency Department (ED) staff engagement is key to early recognition and rapid treatment of the acute stroke patient. Purpose: The purpose of the Stroke Recognition Dinner was to strengthen ED staff engagement in the Stroke Program by promoting rapid assessment and treatment of the acute stroke patient. Reducing Alteplase door-to-needle (DTN) times leads to improved patient outcomes. Methods: Tracking delivery times revealed delays and lost opportunities in Alteplase administration in the ED. DTN times were analyzed before and after the recognition dinner by graphing times and comparing results. The results indicated the percentage of patients receiving Alteplase increased while the DTN times decreased after the March 2016 recognition dinner. Physicians and Nurses have formed a genuine excitement for rapidly assessing and treating the acute stroke patient. Results: There has been a 25minute reduction in overall DTN times from 3 rd quarter 2015 (July) (n = 2) to 3 rd quarter 2016 (July) (n = 6). Times from 1 st quarter 2016 (n = 12) to 3 rd quarter 2016 (July) (n = 6) produced a 13minute reduction in DTN. Alteplase administration increased from 14.7% 2 nd quarter 2015 (n = 15, d = 102) to 16.9% (n = 18, d = 106) 2 nd quarter 2016. Conclusion: Recognizing and rewarding Emergency Department staff improved staff engagement thereby increasing Alteplase administration, decreasing DTN times, and improving patient outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.008 |
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".