Time Course and Clinical Relevance of Neurological Deterioration After Endovascular Recanalization Therapy for Anterior Circulation Large Vessel Occlusion Stroke
Bibliographic record
Abstract
Neurological deterioration (ND) is a devastating complication for patients with ischemic stroke after endovascular recanalization therapy (EVT). We aimed to investigate the time course and clinical relevance of ND after EVT. Consecutive patients with acute ischemic stroke who underwent EVT for large arterial occlusions of the anterior cerebral circulation were enrolled. The National Institutes of Health Stroke Scale (NIHSS) scores were assessed before EVT, at the end of EVT, at 24 h (d1), on day 3 (d3), on day 15 (d15), at discharge and anytime when ND was indicated. ND was defined as an increase of ≥ 4 points in the NIHSS score and was divided into acute ND (AD, within 24 h), subacute ND (SD, d1–d3), and delayed ND (DD, d3–d15 or discharge). Using multivariable logistic regression analysis, we explored predictors and outcomes of ND at different time periods. As a result, of 343 patients, 129 (37.6%) experienced ND, including 90 (26.2%) with AD, 27 (7.9%) with SD and 12 (3.5%) with DD. Multivariable logistic regression analysis revealed that history of hypertension, cardioembolic stroke, lower Alberta Stroke Program Early Computed Tomography Score (ASPECTS), and poor collaterals were significantly associated with an increased risk of AD; history of hypertension, lower ASPECTS, poor collaterals, and unsuccessful recanalization, with SD; and high admission NIHSS score, with DD. In addition, patients who experienced AD (OR = 10.22, P < 0.001), SD (OR = 15.89, P = 0.004), or DD (OR = 8.31, P = 0.015) were more likely to have poor outcomes. ND was a strong predictor of poor stroke outcomes. Management of related risk factors at different ND time periods might improve the prognosis of EVT.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".