Association of Minimal Clinically Important Difference With Infarct Volume in Clinical Poststroke Outcomes
Bibliographic record
Abstract
BACKGROUND: Infarct volume is an imaging biomarker of ischemic tissue injury strongly correlated with poststroke outcomes and reperfusion benefit. We aimed to evaluate the follow-up infarct volume (FIV) difference corresponding to a 1%, 5%, and 10% increase in probability of excellent functional outcomes after intravenous thrombolysis. METHODS: Data are from the AcT trial (Alteplase Compared to Tenecteplase in Patients With Acute Ischemic Stroke). Patients with 24-hour infarct volume segmentations were included. The primary outcome was excellent functional outcome (modified Rankin Scale score=0-1). Associations between outcomes and FIV were investigated with regression analyses adjusted for covariates. Sensitivity analyses examined patients with noncontrast computed tomography versus magnetic resonance imaging follow-up and those treated versus not treated with endovascular thrombectomy. RESULTS: The study included 1478 patients (median age 74 years [interquartile range (IQR), 63-83], 48.2% female). Median FIV was 2.7 mL (IQR, 0-18.2), and 527 (35.7%) achieved excellent functional outcome. FIV was associated with excellent functional outcome: adjusted odds ratio, 0.97 (95% CI, 0.96-0.98) per 1-mL FIV increase. The median ΔFIV corresponding to a 1%, 5%, and 10% increase in adjusted probability of excellent functional outcomes were 1.5 mL (IQR, 1.4-1.9), 7.3 mL (IQR, 6.8-9.3), and 14.6 mL (IQR, 13.6-18.2), respectively. Results were consistent across follow-up imaging modality and endovascular thrombectomy subgroups. CONCLUSIONS: In patients with acute ischemic stroke treated with intravenous thrombolysis, differences of ~1.5 mL, ~7 mL, and ~15 mL in FIV corresponded to a 1%, 5%, and 10% higher absolute probability of achieving excellent functional outcomes. These findings could inform the design of future clinical Phase 2 and proof-of-principle stroke trials that aim to use FIV as a key outcome measure.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".