Abstract TP400: Intravenous Glibenclamide Reduces Water Uptake and Mass Effect in Large Hemispheric Infarction (GAMES-RP Study)
Bibliographic record
Abstract
Introduction: Prior studies have shown a linear relationship between CT-derived density and brain water uptake. We sought to determine whether intravenous (IV) glibenclamide (glyburide; BIIB093) would reduce water uptake and mass effect on serial CT scans from patients enrolled in the phase 2 GAMES-RP trial. Methods: Non-contrast CT scans performed between admission and day 7 (n=263) were analyzed in patients in the GAMES-RP modified intention-to-treat sample. Midline shift (MLS) was measured at the level of maximal lateral displacement of the septum pellucidum. CT-derived radiodensity was measured in pre-defined regions of interest (ROI) within the infarct. The same ROIs were mirrored to the contralateral hemisphere. The change in CT radiodensity (change in water uptake) was averaged among gray and white matter regions separately, and across both regions together. Repeated measures mixed effects models were used to assess the effect of IV glibenclamide on MLS or CT-measured water uptake. Results: There was a median of 3 CT scans [IQR 2-5] per patient during the first 7 days after stroke. Greater change in water uptake was associated with more MLS (β=0.17, 95% CI 0.15 to 0.20, p <0.0001). Using repeated measures models, treatment with IV glibenclamide was associated with reduced water uptake (β= -2.25, 95% CI -4.37 to -0.13, p =0.037) and reduced MLS (β= -1.78, 95% CI -2.87 to -0.67, p =0.002), and adjusted for time. Treatment with IV glibenclamide reduced both gray and white matter water uptake independently. In mediation analysis, gray matter edema (β=0.10, 95% CI 0.05 to 0.16, p <0.001) mediated a greater proportion of MLS when compared to white matter (β=0.02, 95% CI -0.03 to 0.08, p =0.427). Conclusions: IV glibenclamide reduced water uptake and MLS after large hemispheric infarction. A phase 3 study is underway to determine whether IV glibenclamide treatment improves clinical 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".