Presence of Deep White Matter Lesions on Diffusion-Weighted Imaging Is a Negative Predictor of Early Dramatic Improvement after Intravenous Tissue Plasminogen Activator Thrombolysis
Bibliographic record
Abstract
BACKGROUND: The impact of deep white matter lesions observed at the corona radiata on diffusion-weighted MRI (DWI-W lesions) on the clinical recovery of patients after tissue plasminogen activator (tPA) therapy is unclear. Our goal was to elucidate whether DWI findings before tPA could predict clinical recovery. METHODS: A total of 83 consecutive patients with hyperacute anterior circulation ischemic stroke were enrolled. All patients underwent MRI within 3 h and received intravenous tPA. The relationships among the Alberta Stroke Program Early CT Score (ASPECTS) on DWI (DWI-ASPECTS), DWI-W lesions, early dramatic improvement (> or =10-point reduction in the total National Institutes of Health Stroke Scale, NIHSS, score or a total NIHSS score of 0-2 after 24 h), early improvement (> or =4-point reduction in the total NIHSS score after 24 h) and worsening (> or =4-point increase in the total NIHSS score after 24 h) were assessed. RESULTS: The median of the baseline DWI-ASPECTS value was 9 (range: 5-10), and DWI-W lesions were found in 36 patients (43%). Patients with early dramatic improvement had a shorter time from onset to tPA (116.1 +/- 34.9 vs. 133.2 +/- 33.1 min; p = 0.0281) and higher DWI-ASPECTS (medians: 9 vs. 9; p = 0.0568). DWI-W lesions were seen less frequently in patients with than without early dramatic improvement (26 vs. 54%; p = 0.0213). Multivariate logistic regression analysis demonstrated that absence of DWI-W lesions (OR: 1.80; 95% CI: 1.08-3.13; p = 0.0279), higher ASPECTS (OR: 1.56; 95% CI: 1.06-2.46; p = 0.0346) and shorter time from onset to tPA (OR: 0.98; 95% CI: 0.97-0.99; p = 0.0429) were independent predictors of early dramatic improvement. CONCLUSIONS: DWI-ASPECTS and DWI-W lesions appear to be useful tools for predicting early dramatic improvement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".