Impact of workflow times on successful reperfusion after endovascular treatment in the late time window
Bibliographic record
Abstract
Background and Purpose Successful and complete reperfusion should be the aim of every endovascular thrombectomy (EVT) procedure. However, the effect of time delays on successful reperfusion in late window stroke patients presenting 6-to-24 h from onset has not been investigated. Materials and Methods We pooled individual patient-level data from seven trials and registries for anterior circulation stroke patients treated with EVT between 6 and 24 h from onset. We explored the impact of delays across multiple interval times, including onset to hospital arrival; hospital arrival to arterial puncture; imaging to arterial puncture; and onset to arterial puncture. Our primary outcome was successful reperfusion, defined as a modified thrombolysis in cerebral infarction (mTICI) score of 2b–3. Logistic regression analyses were performed to assess the association between each of the interval times and successful reperfusion. Results We included 608 patients. The median age was 70 years (IQR 58–79), and 307 (50.5%) were females. Successful reperfusion was achieved in 494 (81.2%) patients. Patients with successful reperfusion had lower NIHSS scores (median 15 [IQR11–19] vs 17 [11–21], p = .02) and significantly shorter hospital arrival to arterial puncture time (90 min [60–150] vs 110 min [84.5–150], p = .01) than unsuccessful reperfusion. The odds of successful reperfusion decreased by 15% for every one-hour delay in arrival-to-puncture time (adjusted odds ratio 0.85, 95% CI: 0.75–0.95). Other workflow times did not impact the rate of successful reperfusion. Conclusion Faster hospital arrival to arterial puncture time is associated with higher odds of successful reperfusion in late window stroke patients.
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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 | 0.021 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".