Predictors of Fatal Brain Edema in Massive Hemispheric Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Early identification of stroke patients at risk for fatal brain edema may be useful in selecting patients for aggressive interventions. Prior studies suggested that early nausea/vomiting and major hypodensity on baseline computed tomography (CT) were predictive of herniation. METHODS: This study was a retrospective multicenter case-control study of patients with large middle cerebral artery (MCA) strokes admitted within 48 hours of symptom onset. Medical records, laboratory data, and CT scans were analyzed. Cases, defined as patients who died of massive brain swelling, were compared with all remaining patients as controls. RESULTS: Two hundred one patients with large MCA strokes were identified: 94 (47%) died of brain swelling, 12 (6%) died of non-neurological causes, and 95 (47%) survived at day 30. Multivariable analysis, adjusted for age and clustered by center, identified the following predictors of fatal brain edema: history of hypertension (OR 3.0, 95% CI 1.2 to 7.6, P=0.02), history of heart failure (OR 2.1, 95% CI 1.5 to 3.0, P<0.001), elevated white blood cell count (OR 1.08 per 1000 white blood cells/microL, 95% CI 1.01 to 1.14, P=0.02), >50% MCA hypodensity (OR 6.3, 95% CI 3.5 to 11.6, P<0.001), and involvement of additional vascular territories (anterior cerebral artery, posterior cerebral artery, or anterior choroidal artery; OR 3.3, 95% CI 1.2 to 9.4, P=0.02). Initial level of consciousness, National Institutes of Health Stroke Scale score, early nausea/vomiting, and serum glucose were associated with neurological death in bivariable but not multivariable analyses. CONCLUSIONS: Among patients with large MCA infarctions, an increased risk of fatal brain edema is associated with history of hypertension or heart failure, increased baseline white blood cell count, major early CT hypodensity involving >50% of the MCA territory, and involvement of additional vascular territories. These data confirm and expand on prior research with a broad-based patient population. The presence of these risk factors identifies those stroke patients who may require aggressive therapeutic approaches.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".