Risk factors of unexplained early neurological deterioration after treatment for ischemic stroke due to large vessel occlusion: a post hoc analysis of the HERMES study
Bibliographic record
Abstract
BACKGROUND: Early neurological deterioration (END) after endovascular treatment (EVT) in patients with anterior circulation acute ischemic stroke (AIS) is associated with poor outcome. END may remain unexplained by parenchymal hemorrhage (UnEND). We aim to analyze the risk factors of UnEND in the medical management (MM) and EVT arms of the HERMES study. METHODS: We conducted a post-hoc analysis of anterior AIS patients who underwent EVT for proximal anterior occlusions. Risk factors of UnEND, defined as a worsening of ≥4 points between baseline National Institutes of Health Stroke Scale (NIHSS) and NIHSS at 24 hours without hemorrhage, were compared between both arms using mixed logistic regression models adjusted for baseline characteristics. An interaction analysis between the EVT and MM arms for risk factors of UnEND was conducted. RESULTS: Among 1723 patients assessable for UnEND, 160 patients experienced an UnEND (9.3%), including 9.1% (78/854) in the EVT arm and 9.4% (82/869) in the MM arm. There was no significant difference in the incidence of UnEND between the two study arms. In the EVT population, independent risk factors of UnEND were lower baseline NIHSS, higher baseline glucose, and lower collateral grade. In the MM population, the only independent predictor of UnEND was higher baseline glucose. However, we did not demonstrate an interaction between EVT and MM for baseline factors as risk factors of UnEND. UnEND was, similarly in both treatment groups, a significant predictor of unfavorable outcome in both the EVT (p<0.001) and MM (p<0.001) arms. CONCLUSIONS: UnEND is not an uncommon event, with a similar rate which ever treatment arm is considered. In the clinical scenario of AIS due to large vessel occlusion, no patient-related factor seems to increase the risk for UnEND when treated by EVT compared with MM.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".