Association of follow-up infarct volume with functional outcome in acute ischemic stroke: a pooled analysis of seven randomized trials
Bibliographic record
Abstract
BACKGROUND: Follow-up infarct volume (FIV) has been recommended as an early indicator of treatment efficacy in patients with acute ischemic stroke. Questions remain about the optimal imaging approach for FIV measurement. OBJECTIVE: To examine the association of FIV with 90-day modified Rankin Scale (mRS) score and investigate its dependency on acquisition time and modality. METHODS: Data of seven trials were pooled. FIV was assessed on follow-up (12 hours to 2 weeks) CT or MRI. Infarct location was defined as laterality and involvement of the Alberta Stroke Program Early CT Score regions. Relative quality and strength of multivariable regression models of the association between FIV and functional outcome were assessed. Dependency of imaging modality and acquisition time (≤48 hours vs >48 hours) was evaluated. RESULTS: Of 1665 included patients, 83% were imaged with CT. Median FIV was 41 mL (IQR 14-120). A large FIV was associated with worse functional outcome (OR=0.88(95% CI 0.87 to 0.89) per 10 mL) in adjusted analysis. A model including FIV, location, and hemorrhage type best predicted mRS score. FIV of ≥133 mL was highly specific for unfavorable outcome. FIV was equally strongly associated with mRS score for assessment on CT and MRI, even though large differences in volume were present (48 mL (IQR 15-131) vs 22 mL (IQR 8-71), respectively). Associations of both early and late FIV assessments with outcome were similar in strength (ρ=0.60(95% CI 0.56 to 0.64) and ρ=0.55(95% CI 0.50 to 0.60), respectively). CONCLUSIONS: In patients with an acute ischemic stroke due to a proximal intracranial occlusion of the anterior circulation, FIV is a strong independent predictor of functional outcome and can be assessed before 48 hours, oneither CT or MRI.
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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 | 0.042 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".