Postthrombolysis Blood Pressure Elevation Is Associated With Hemorrhagic Transformation
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Reliable predictors of hemorrhagic transformation (HT) after stroke thrombolysis have not been identified. We analyzed hemorrhage in a randomized trial of tissue plasminogen activator (t-PA) vs placebo in ischemic stroke patients. We hypothesized that acute diffusion-weighted imaging (DWI) lesion volumes would be larger and blood pressures would be higher in patients with HT. METHODS: HT was assessed 2 to 5 days after treatment in 97 patients. Hemorrhage was assessed by using susceptibility-weighted imaging sequences and was classified as petechial hemorrhagic infarction (HI) or parenchymal hematoma (PH). RESULTS: PH was more frequent in t-PA- (11/49) than in placebo- (4/48) treated patients (P=0.049). Patients with PH had larger DWI lesion volumes (63.1+/-56.1 mL) than did those without HT (27.6+/-39.0 mL, P=0.033). There were no differences in baseline systolic blood pressure (SBP) between patients with and without hemorrhage. Weighted average SBP 24 hours after treatment was higher in patients with PH (159.4+/-18.8 mL, P<0.011) relative to those without HT (143.1+/-20.0 mL). Multinomial logistic regression indicated that PH was predicted by DWI lesion volume (odds ratio=1.16 per 10 mL; 95% CI, 1.03 to 1.30), atrial fibrillation (odds ratio=9.33; 95% CI, 2.30 to 37.94), and 24-hour weighted average SBP (odds ratio=1.59 per 10 mm Hg; 95% CI, 1.14 to 2.23). CONCLUSIONS: Pretreatment DWI lesion volume and postthrombolysis BP are both predictive of HT. Consideration should be given to excluding patients with very large baseline DWI volumes from t-PA therapy and to more stringent BP control after stroke thrombolysis.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".