Reducing Door-to-Needle Time for Tissue Plasminogen Activator Administration in a Community Hospital: An Operations Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The benefit of tissue plasminogen activator (tPA) in acute ischemic stroke is time dependent. A 15-minute decrease in door-to-needle (DTN) time has been associated with increased odds of ambulating independently, faster discharge, and decreased odds of death. We investigated common causes of delay in DTN times in a community hospital setting in order to identify areas for improvement. METHODS: A retrospective medical record review was conducted at a 574-bed community hospital. This included 100 patients who received tPA from 2016 to 2019. Time segments were classified a priori to reflect key work elements from the time between hospital arrival to tPA and recorded for each chart. Linear regression models were used to identify work elements associated with increased DTN time. RESULTS: Median DTN time was 54:29 minutes. Linear regression analyses determined that differences in NIHSS score (P = .030), triage to computed tomography (CT) start (P = .017), triage to stroke physician page (P = .016), and CT report to tPA administration (P < .001) were associated with increased DTN time. CT report to tPA administration was most strongly associated with a Pearson coefficient of 0.868 (P < .001) with increased DTN time. CONCLUSIONS: The DTN time at our institution was above the recommended target. Our findings suggest that reducing the CT report time interval may decrease DTN 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".