Postoperative neutrophil-to-lymphocyte ratio predicts malignant cerebral edema following endovascular treatment for acute ischemic stroke
Bibliographic record
Abstract
Background and purpose Malignant cerebral edema (MCE) is one of serious complications with high mortality following endovascular treatment (EVT) for acute ischemic stroke (AIS) with large vessel occlusion. We aimed to investigate the relationship between postoperative neutrophil-to-lymphocyte ratio (NLR) and MCE after EVT. Methods The clinical and imaging data of 175 patients with AIS of anterior circulation after EVT were studied. Admission and postoperative NLR were determined. The presence of MCE was evaluated on the computed tomography performed 24 h following EVT. The clinical outcomes were measured using the modified Rankin Scale (mRS) at 90-day after onset. Univariate and multivariate regression analyses were used to analyze the relationship between postoperative NLR and MCE. Optimal cutoff values of postoperative NLR to predict MCE were defined using receiver operating characteristic analysis. Results MCE was observed in 24% of the patients who underwent EVT and was associated with a lower rate of favorable clinical outcomes at 90-day. Multivariate logistic regression analysis demonstrated that baseline Alberta Stroke Program Early CT Score (ASPECT) score (OR = 0.614, 95% CI 0.502–0.750, p = 0.001), serum glucose (OR = 1.181, 95% CI 1.015–1.374, p = 0.031), and postoperative NLR (OR = 1.043, 95% CI 1.002–1.086, p = 0.041) were independently associated with MCE following EVT for AIS with large vessel occlusion. Postoperative NLR had an area under the receiver operating characteristic curve of 0.743 for prediction MCE, and the optimal cutoff value was 6.15, with a sensitivity and specificity of 86.8% and 55%. Conclusion Elevated postoperative NLR is independently associated with malignant brain edema following EVT for AIS with large vessel occlusion, and may serve as an early predictive indicator for MCE after 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".