Abstract WMP7: Poor Reperfusion After Endovascular Therapy is Associated With Worse Outcomes Than No Intervention in the HERMES Analysis of Seven Randomized Trials
Bibliographic record
Abstract
Introduction: Although the role of successful reperfusion in improved outcome after endovascular treatment (EVT) for stroke is known, there has been limited evaluation of the effects of poor reperfusion. We compared results in the HERMES collaboration between standard care and those in the EVT group with poor reperfusion. Methods: Patient-level data were pooled in this meta-analysis of seven randomized trials comparing endovascular thrombectomy with standard care in anterior ischemic stroke. Functional outcome was assessed by ordinal analysis of the modified Rankin scale (mRS) at 90 days, comparing standard care versus EVT subjects achieving mTICI of 0 or 1 post-procedure. Analyses were adjusted for baseline prognostic variables to correct for potential imbalances. Results: The meta-analysis included 877 subjects in the standard care group and 78 in the EVT group with mTICI 0-1 per the HERMES central imaging core laboratory. At baseline, the EVT cohort had higher NIHSS (median 19 vs 17, p=0.011), but was not significantly different from standard care in other characteristics including age, sex, ASPECTS, time to randomization, site of occlusion and alteplase administration. Subjects with poor reperfusion in the EVT group had worse mRS at 90 days than standard care, unadjusted (p=0.003) and after adjustment for baseline characteristics (common odds ratio 0.59 (0.38-0.91), p=0.016). Fewer subjects in the EVT poor-reperfusion cohort achieved mRS 0, mRS 0-1 and all other dichotomized mRS cutpoints (Figure 1). Symptomatic intracranial hemorrhage was not different between groups (3.9% vs 3.5%, p=0.75). Conclusion: In recent endovascular trials, poor reperfusion after EVT was associated with worse outcomes than standard care in recent endovascular trials. This suggests that additional efforts to achieve reperfusion by EVT should be encouraged if deemed safe to do so.
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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 | 0.038 | 0.061 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.039 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".