Predictors of Unexplained Early Neurological Deterioration After Endovascular Treatment for Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Although the efficacy of endovascular treatment (EVT) in patients with anterior circulation ischemic stroke (AIS) is well documented, early neurological deterioration after EVT remains a serious issue associated with poor outcome. Besides obvious causes, such as lack of reperfusion, procedural complications, or parenchymal hemorrhage, early neurological deterioration may remain unexplained (UnEND). Our aim was to investigate predictors of UnEND after EVT in patients with AIS. METHODS: Patients who underwent EVT for AIS, with an initial National Institutes of Health Stroke Scale score >5, Alberta Stroke Program Early CT Score ≥6, and included in a multicenter prospective observational registry were analyzed. Predictors of UnEND, defined as ≥4-point increase in the National Institutes of Health Stroke Scale score between baseline and day 1 after EVT, were determined via center-adjusted analyses. RESULTS: Among the 1925 included in the analysis, 128 UnEND (6.6%) were recorded. In multivariate analysis, predictors of UnEND were diabetes mellitus (odds ratio [OR], 2.17 [95% CI, 1.32-3.56]), prestroke modified Rankin Scale score ≥2 (OR, 2.22 [95% CI, 1.09-4.55]), general anesthesia (OR, 2.55 [95% CI, 1.51-4.30]), admission systolic blood pressure (OR, 1.10 [95% CI, 1.01-1.20]), age (OR, 1.38 [95% CI, 1.14-1.67]), number of passes (OR, 1.16 [95% CI, 1.04-1.28]), direct admission or not to a comprehensive stroke center (OR, 0.49 [95% CI, 0.30-0.81]), and initial National Institutes of Health Stroke Scale score (OR, 0.65 [95% CI, 0.52-0.81]). CONCLUSIONS: Severely impaired AIS patients with nonmodifiable factors are more likely to develop UnEND. Some modifiable predictors of UnEND such as the number of EVT passes could be the object of improvement in AIS management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".