Endovascular therapy in acute ischemic stroke with poor reperfusion is associated with worse outcomes compared with best medical management: a HERMES substudy
Bibliographic record
Abstract
BACKGROUND: Functional outcomes in patients with acute ischemic stroke (AIS) with large vessel occlusion (LVO) undergoing endovascular treatment (EVT) with poor reperfusion were compared with patients with AIS-LVO treated with best medical management only. METHODS: Data are from the HERMES collaboration, a patient-level meta-analysis of seven randomized EVT trials. Baseline characteristics and functional outcomes (modified Rankin Scale (mRS) score at 90 days) were compared between patients with poor reperfusion (defined as modified Thrombolysis in Cerebral Infarction Score 0-1 on the final intracranial angiography run as assessed by the central imaging core laboratory) and patients in the control arm with multivariable logistic ordinal logistic regression adjusted for pre-specified baseline variables. RESULTS: 972 of 1764 patients from the HERMES collaboration were included in the analysis: 893 in the control arm and 79 in the EVT arm with final mTICI 0-1. Patients with poor reperfusion who underwent EVT had higher baseline National Institutes of Health Stroke Scale than controls (median 19 (IQR 15.5-21) vs 17 (13-21), P=0.011). They also had worse mRS at 90 days compared with those in the control arm in adjusted analysis (median 4 (IQR 3-6) vs median 4 (IQR 2-5), adjusted common OR 0.59 (95% CI 0.38 to 0.91)). Symptomatic intracranial hemorrhage was not different between the two groups (3.9% vs 3.5%, P=0.75, adjusted OR 0.94 (95% CI 0.23 to 3.88)). CONCLUSION: Poor reperfusion after EVT was associated with worse outcomes than best medical management, although no difference in symptomatic intracranial hemorrhage was seen. These results emphasize the need for additional efforts to further improve technical EVT success rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".