Effect of intravenous alteplase on ischaemic lesion water homeostasis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Intravenous (IV) lysis with alteplase is known to increase biomarkers of blood-brain barrier breakdown and has therefore been associated with secondary injuries such as hemorrhagic transformation. The impact of alteplase on brain edema formation, however, has not been investigated yet. The purpose was to examine the effects of IV alteplase on ischaemic lesion water homeostasis differentiated from final tissue infarct in patients with and without successful endovascular therapy (sET). METHODS: In all, 232 middle cerebral artery stroke patients were analyzed. 147 patients received IV alteplase, of whom 106 patients received subsequent sET. Out of 85 patients without IV alteplase, 50 received sET. Ischaemic brain edema was quantified at admission and follow-up computed tomography using quantitative lesion net water uptake (NWU) and its difference was calculated (ΔNWU). The relationship of alteplase on ΔNWU and edema-corrected final infarct volume was analyzed using univariate and multivariate linear regression models. RESULTS: The mean ΔNWU was 11.8% (SD 7.9) in patients with alteplase and 11.5% (SD 8.3) in patients without alteplase (P = 0.8). Alteplase was not associated with lowered ΔNWU whilst being associated with reduced edema-corrected tissue infarct volume [-27.4 ml, 95% confidence interval (CI) -49.4 to -5.4 ml; P = 0.02], adjusted for the Alberta Stroke Program Early Computed Tomography Score and recanalization status. In patients with sET, ΔNWU was 10.5% (95% CI 6.3%-10.5%) for patients with IV alteplase and 8.4% (95% CI 9.1%-12.0%) for patients without IV alteplase. CONCLUSION: The application of IV alteplase did not significantly alter ischaemic lesion water homeostasis but was associated with reduced edema-corrected tissue infarct volume, which might be directly linked to improved functional outcome.
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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 | 0.000 | 0.002 |
| 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".