Large Ischemic Lesions on Diffusion-Weighted Imaging Done Before Intravenous Tissue Plasminogen Activator Thrombolysis Predicts a Poor Outcome in Patients With Acute Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: MRI is useful for detecting early ischemic lesions before administration of tissue plasminogen activator in patients with hyperacute ischemic stroke. However, it is unclear whether early ischemic change seen on diffusion-weighted imaging (DWI) can be used to predict patient outcomes. METHODS: Consecutive patients with anterior circulation ischemic stroke treated with tissue plasminogen activator within 3 hours of stroke onset were prospectively studied. The National Institutes of Health Stroke Scale score was obtained before and 7 days after tissue plasminogen activator administration. MRI, including DWI, was done before tissue plasminogen activator thrombolysis. The relationship between the DWI Alberta Stroke Programme Early CT Score (ASPECTS) and patients' outcomes was assessed. RESULTS: The subjects consisted of 49 consecutive patients with stroke (27 males; mean age, 72.9+/-10.3 years). The median (range) of the baseline DWI ASPECTS value was 9 (3-10). Dramatic improvement was seen in one of 8 patients with an ASPECTS < or = 5 compared with 21 of 41 patients with a DWI ASPECTS > 5 (P=0.0592). On the other hand, worsening was noted more frequently in patients with a DWI ASPECTS < or = 5 (3 of 8 patients) than in patients with an ASPECTS > 5 (4 of 41 patients; P=0.0753). Bad outcome was seen more frequently in patients with a DWI ASPECTS < or = 5 (6 of 8 patients) than in patients with a DWI ASPECTS > 5 (2 of 41 patients; P<0.0001). Multivariate logistic regression analysis demonstrated that a DWI ASPECTS < or = 5 was the only independent predictor of a bad outcome (OR, 33.4; 95% CI, 2.7 to 410.8; P=0.0062). CONCLUSIONS: DWI ASPECTS appears to be a reliable tool for predicting bad outcome. Patients with a DWI ASPECTS > 5 should be considered eligible for tissue plasminogen activator therapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".