Abstract TMP65: What Changes Improve Door-to-Needle Times? Results From a Single Center Door-to-Needle Improvement Initiative
Bibliographic record
Abstract
Background: The benefit of thrombolysis is highly time dependent. Strategies and system changes to reduce door-to-needle time (DNT) have been proposed but the effectiveness of such strategies has not been fully evaluated. Hypothesis: Specific change strategies to the system of delivering thrombolysis significantly improve DNT. Stroke severity also affects DNT significantly. Methods: The Hurry Acute Stroke Treatment and Evaluation (HASTE) project was implemented in 3 phases at a single academic medical center to reduce DNT using four strategies. In HASTE-I (Jun 6 2012 - Jun 5 2013), baseline performance was analyzed with no changes made to stroke treatment. In HASTE-II (Jun 6 2013 - Jan 24 2015), three changes were implemented: 1) a STAT! stroke protocol to pre-notify the stroke team of the severity of incoming stroke patients; 2) administering tPA in the CT scanner; and 3) registering the patient as unknown prior to exact identification, to allow immediate order entry in our electronic health system. In HASTE-III (Jan 25 2015 - Jun 29 2015), we implemented a process to bring the patient directly to CT on the EMS stretcher. Decrease in DNT was analyzed using Wilcoxon rank sum and Kruskal Wallis tests, and multivariable linear regression. Log transformed DNT was modeled using a backward selection approach. Results: There were 350 patients treated with tPA during the project. The results of the univariable and multivariable analyses are shown in the table. In univariable analyses, the following strategies improved DNT: STAT! stroke, patient registered as unknown , and stretcher to CT. Additionally, DNT was lower if tPA was administered in the CT. The stroke severity also affected DNT. Multivariable regression showed the following factors to be significant: giving tPA in the CT, stretcher to CT, patient registered as unknown , and stroke severity. Conclusions: Stretcher to CT, patient registered as unknown , and administering tPA in CT were most efficacious in reducing DNT.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".