Abstract WP13: Time to Treatment With Endovascular Reperfusion Therapy and Outcome From Acute Ischemic Stroke in the National US GWTG-Stroke Population
Bibliographic record
Abstract
Background: Randomized trials have shown the benefit of endovascular reperfusion therapy (ERT) in acute ischemic stroke (AIS) is time dependent. However, generalizability to routine practice is uncertain; and modest sample sizes have limited characterization of the degree to which onset to treatment time influences outcome from ERT. Methods: We analyzed data of 6756 AIS patients treated with ERT at 231 hospitals between January 2015 to December 2016. Multivariable logistic regression modeling was conducted to evaluate the independent impact of onset to puncture (OTP) and door to puncture (DTP) time on efficacy and safety outcomes. Results: Among the 6756 patients, median age was 71, 51.2% were female, and NIHSS was 17 (IQR 12-22). Median OTP was 230m (IQR 170-305) and DTP 87m (IQR 62-116). Substantial reperfusion (TICI 2b-3) was in 85.9%. At discharge, 36.9% had independent ambulation, 27.8% were discharged to home, and 23.0% were functionally independent (mRS 0-2). Symptomatic intracranial hemorrhage (sICH) occurred in 6.7% and in-hospital mortality/hospice in 19.6%. For OTP, time-outcome relationships were nonlinear, with steeper slope in 0-270m than 271-480m (see Table and Figure). DTP showed a similar nonlinear time-outcome relationship, with steeper benefit decline in the 30-120m than 121-180m period. Conclusions: In this national registry, faster endovascular therapy start, after onset and after arrival, was associated with better ambulation and functional independence at discharge, and reduced sICH and mortality/hospice. These findings support intensive efforts to accelerate hospital presentation and endovascular treatment in patients with stroke.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".