Regional Very Low Cerebral Blood Volume Predicts Hemorrhagic Transformation Better Than Diffusion-Weighted Imaging Volume and Thresholded Apparent Diffusion Coefficient in Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Currently, diffusion-weighted imaging (DWI) lesion volume is the most useful magnetic resonance imaging predictor of hemorrhagic transformation (HT). Preliminary studies have suggested that very low cerebral blood volume (VLCBV) predicts HT. We compared HT prediction by VLCBV and DWI using data from the EPITHET study. METHODS: Normal-percentile CBV values were calculated from the nonstroke hemisphere. Whole-brain masks with CBV thresholds of the <0, 2.5, 5, and 10th percentiles were created. The volume of tissue with VLCBV was calculated within the acute DWI ischemic lesion. HT was graded as per ECASS criteria. RESULTS: HT occurred in 44 of 91 patients. Parenchymal hematoma (PH) occurred in 13 (4 symptomatic) and asymptomatic hemorrhagic infarction (HI) in 31. The median volume of VLCBV was significantly higher in cases with PH. VLCBV predicted HT better than DWI lesion volume and thresholded apparent diffusion coefficient lesion volume in receiver operating characteristic analysis and logistic regression. A cutpoint at 2 mL VLCBV with the <2.5th percentile had 100% sensitivity for PH and, in patients treated with tissue plasminogen activator, defined a population with a 43% risk of PH (95% CI, 23% to 66%, likelihood ratio=16). VLCBV remained an independent predictor of PH in multivariate analysis with traditional clinical risk factors for HT. CONCLUSIONS: VLCBV predicted HT after thrombolysis better than did DWI or apparent diffusion coefficient volume in this large patient cohort. The advantage was greatest in patients with smaller DWI volumes. Prediction was better in patients who recanalized. If validated in an independent cohort, the addition of VLCBV to prethrombolysis decision making may reduce the incidence of HT.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".