Initial Lesion Volume Is an Independent Predictor of Clinical Stroke Outcome at Day 90
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Age and National Institutes of Health Stroke Scale early after stroke onset have been identified as important determinants of final stroke outcome. We analyzed the Virtual International Stroke Trials Archive (VISTA) database to define the influence of infarct or hemorrhagic volume on clinical outcome after stroke. METHODS: All patients were extracted from VISTA where infarct or hemorrhage volume information was available (n=2538; most images obtained by CT within 72 hours after stroke onset with a subset of MRI data included, volumes calculated by the ABC/2 approximation method). We used multivariate regression models to study the influence of age, National Institutes of Health Stroke Scale at baseline, and initial infarct/hemorrhage volume on clinical outcome (modified Rankin Scale, National Institutes of Health Stroke Scale, mortality) at day 90. RESULTS: We find that in a large cohort of >1800 patients with ischemic stroke, initial lesion size is a strong and independent predictor of stroke outcome in a statistical regression model that also accounts for age and National Institutes of Health Stroke Scale at baseline (P<0.0001). The use of infarct/hemorrhage volume as an additional predictive factor further reduces the fraction of unexplained variance in outcome by approximately 15% (R(2) of 0.41 versus 0.26 in a model without lesion volume). The predictive strength of initial lesion size is only marginally influenced by image modality or time point of image acquisition within the first 72 hours. The model was equally valid for both ischemic and hemorrhagic strokes. CONCLUSIONS: Infarct/hemorrhage volume at baseline together with age and National Institutes of Health Stroke Scale at baseline should be used in the effect analysis of future therapeutic stroke trials to improve power.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".