Advanced imaging improves prediction of hemorrhage after stroke thrombolysis
Bibliographic record
Abstract
OBJECTIVE: Very low cerebral blood volume (VLCBV), diffusion, and hypoperfusion lesion volumes have been proposed as predictors of hemorrhagic transformation following stroke thrombolysis. We aimed to compare these parameters, validate VLCBV in an independent cohort using DEFUSE study data, and investigate the interaction of VLCBV with regional reperfusion. METHODS: The EPITHET and DEFUSE studies obtained diffusion and perfusion magnetic resonance imaging (MRI) in patients 3 to 6 hours from onset of ischemic stroke. EPITHET randomized patients to tissue plasminogen activator (tPA) or placebo, and all DEFUSE patients received tPA. VLCBV was defined as cerebral blood volume<2.5th percentile of brain contralateral to the infarct. Parenchymal hematoma (PH) was defined using European Cooperative Acute Stroke Study criteria. Reperfusion was assessed using subacute perfusion MRI coregistered to baseline imaging. RESULTS: In DEFUSE, 69 patients were analyzed, including 9 who developed PH. The >2 ml VLCBV threshold defined in EPITHET predicted PH with 100% sensitivity, 72% specificity, 35% positive predictive value, and 100% negative predictive value. Pooling EPITHET and DEFUSE (163 patients, including 23 with PH), regression models using VLCBV (p<0.001) and tPA (p=0.02) predicted PH independent of clinical factors better than models using diffusion or time to maximum>8 seconds lesion volumes. Excluding VLCBV in regions without reperfusion improved specificity from 61 to 78% in the pooled analysis. INTERPRETATION: VLCBV predicts PH after stroke thrombolysis and appears to be a more powerful predictor than baseline diffusion or hypoperfusion lesion volumes. Reperfusion of regions of VLCBV is strongly associated with post-thrombolysis PH. VLCBV may be clinically useful to identify patients at significant risk of hemorrhage following reperfusion.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".