Risk of Stroke Recurrence After Intravenous Thrombolysis in Patients with Symptomatic Carotid Stenosis
Bibliographic record
Abstract
ABSTRACT: Objective: To assess if intravenous thrombolysis (IVT) affects the risk of recurrent preoperative cerebrovascular events before carotid surgery or stenting in patients with symptomatic ≥ 50% carotid stenosis. Methods: Three cohorts of symptomatic ≥ 50% carotid stenosis patients were merged. To make the control group relevant, we excluded patients not presenting with stroke on the day of symptom onset. The risk of preoperative cerebrovascular events up to 30 days was compared between the IVT-treated and non-IVT-treated. Results: In total, 316 patients were included, 64 (20%) treated with IVT. Those treated with IVT had similar risk of recurrent ipsilateral ischemic stroke or retinal artery occlusion (12% at day 7, 12% at day 30) as those not treated (9% at day 7, 15% at day 30; adjusted HR 0.9, 95% CI 0.4–2.2). There was a tendency (p = 0.09) towards time-dependency in the data where the recurrence risk was higher in IVT-treated at day 0 (6% in IVT-treated, 1% in non-IVT-treated, OR 5.5, 95% CI 1.2–25.4, p = 0.03). This was not significant when adjusting for co-factors (adjusted OR 4.4, 95% CI 0.9–21.8, p = 0.07) and was offset by a later risk decrease, with no remaining risk difference between IVT-treated and non-IVT-treated at day 7. Conclusions: Intravenous thrombolysis treatment does not seem to affect the risk of recurrent ipsilateral ischemic stroke in patients with symptomatic ≥50% carotid stenosis: The risk is high in both IVT-treated and non-IVT-treated. However, there might be a risk increase on the day of IVT treatment that is offset by a risk decrease during the first week.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".