Pretreatment Diffusion- and Perfusion-MR Lesion Volumes Have a Crucial Influence on Clinical Response to Stroke Thrombolysis
Bibliographic record
Abstract
We hypothesized that pretreatment magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) lesion volumes may have influenced clinical response to thrombolysis in the Echoplanar Imaging Thrombolytic Evaluation Trial (EPITHET). In 98 patients randomized to intravenous (IV) tissue plasminogen activator (tPA) or placebo 3 to 6 h after stroke onset, we examined increasing acute DWI and PWI lesion volumes (Tmax-with 2-sec delay increments), and increasing PWI/DWI mismatch ratios, on the odds of both excellent (modified Rankin Scale (mRS): 0 to 1) and poor (mRS: 5 to 6) clinical outcome. Patients with very large PWI lesions (most had internal carotid artery occlusion) had increased odds ratio (OR) of poor outcome with IV-tPA (58% versus 25% placebo; OR=4.13, P=0.032 for Tmax +2-sec volume >190 mL). Excellent outcome from tPA treatment was substantially increased in patients with DWI lesions <18 mL (77% versus 18% placebo, OR=15.0, P<0.001). Benefit from tPA was also seen with DWI lesions up to 25 mL (69% versus 29% placebo, OR=5.5, P=0.03), but not for DWI lesions >25 mL. In contrast, increasing mismatch ratios did not influence the odds of excellent outcome with tPA. Clinical responsiveness to IV-tPA, and stroke outcome, depends more on baseline DWI and PWI lesion volumes than the extent of perfusion-diffusion mismatch.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".