Abstract 88: Workflow Delays And Outcome Of Endovascular Thrombectomy In The Late Stroke Window:results From A Pooled Multicenter Analysis
Bibliographic record
Abstract
Background: Efficient healthcare workflow leads to faster reperfusion and better functional outcomes of stroke in the early-time window. We investigated the impact of care delays on the outcomes of stroke patients treated with endovascular thrombectomy (EVT) in the late window. Methods: Pooled data from seven randomized clinical trials and registries that only included patients who underwent EVT in the late time window (onset/last known well (LKW) time to imaging time of 6 hours or more) were combined for this analysis. The time intervals from stroke onset to successful reperfusion were analyzed. Logistic regression was used to estimate the likelihood of a functionally independent outcome at 90 days (modified Rankin scale 0-2) for each time interval while adjusting for relevant patients’ characteristics. Negative binomial regression was used to evaluate the relationship between each time interval and the predictors. Results: 584 patients were included in this analysis. The median age was 70 years (IQR: 21), 293 [50.17%] were females, 298 (53.31%) had wake-up strokes, and the median ASPECTS was 8 (IQR: 2). All patients had CT, and CTA imaging, and 360 (61.64%) underwent perfusion imaging. Successful reperfusion was achieved in 469 (80.45%) patients, and 249 (44.54%) had independent outcomes at 90 days. For every 30 minutes delay, the estimated probability of functional independence decreased by 19% for the emergency department (ED) arrival to imaging time interval, by 25% from groin puncture to end of EVT, and by 12% from ED arrival to end of EVT. Older age and higher NIHSS were associated with longer time from imaging to groin puncture. However, only age was associated with a longer estimated times from stroke onset/LKW to arrival in ED and from stroke onset/LKW to the end of EVT. Conclusion: Faster in-hospital care is associated with improved functional independence among late-window patients. Page 1
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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 | 0.027 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.018 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".