Effect of atrial fibrillation on endovascular thrombectomy for acute ischemic stroke. A meta-analysis of individual patient data from six randomised trials: Results from the HERMES collaboration
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Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation is an important risk factor for ischemic stroke, and is associated with an increased risk of poor outcome after ischemic stroke. Endovascular thrombectomy is safe and effective in acute ischemic stroke patients with large vessel occlusion of the anterior circulation. This meta-analysis aims to investigate whether there is an interaction between atrial fibrillation and treatment effect of endovascular thrombectomy, and secondarily whether atrial fibrillation is associated with worse outcome in patients with ischemic stroke due to large vessel occlusion. METHODS: Individual patient data were from six of the recent randomised clinical trials (MR CLEAN, EXTEND-IA, REVASCAT, SWIFT PRIME, ESCAPE, PISTE) in which endovascular thrombectomy plus standard care was compared to standard care alone. Primary outcome measure was the shift on the modified Rankin scale (mRS) at 90 days. Secondary outcomes were functional independence (mRS 0-2) at 90 days, National Institutes of Health Stroke Scale score at 24 h, symptomatic intracranial hemorrhage and mortality at 90 days. The primary effect parameter was the adjusted common odds ratio, estimated with ordinal logistic regression (shift analysis); treatment effect modification of atrial fibrillation was assessed with a multiplicative interaction term. RESULTS: -value for interaction: 0.58) and secondary outcomes. Regardless of treatment allocation, we found no difference in primary outcome (mRS at 90 days: aOR 1.11 (95% CI 0.89-1.38) and secondary outcomes between patients with and without atrial fibrillation. CONCLUSION: We found no interaction of atrial fibrillation on treatment effect of endovascular thrombectomy, and no difference in outcome between large vessel occlusion stroke patients with and without atrial fibrillation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it