Final infarct volume discriminates outcome in mild strokes
Bibliographic record
Abstract
INTRODUCTION: Knowledge of whether final infarct volume (FIV) predicts disability after mild stroke is limited. We sought to determine if FIV could differentiate good versus poor outcome after mild stroke. METHODS: We retrospectively identified 65 patients with mild stroke (National Institutes of Health Stroke Scale≤5) in a multicenter registry of 2453 patients. We evaluated associations between FIV and clinical outcome and evaluated the optimal FIV threshold that discriminated favorable (modified Rankin scale (mRS) 0-1) versus poor (mRS 2-6) outcome. RESULTS: The FIV cut-point of 20 mL differentiated favorable and poor outcomes (area under curve (AUC) 0.73, 95% confidence interval: 0.58-0.88). Favorable outcome was observed in 37/45 (82%) with FIV<20 mL, compared to 5/14 (36%) with FIV≥20 mL (p<0.01). FIV≥20 mL remained strongly associated with poor outcome independent of age, gender, stroke severity, Alberta Stroke Program Early CT Score (ASPECTS), and proximal arterial occlusion. CONCLUSION: In our small sample size, an FIV of 20 mL best differentiated between the likelihood of good versus poor outcome in patients with mild stroke. Further validation of infarct volume as a surrogate marker in mild stroke is warranted.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".