ATNT-10DOES VALPROIC ACID IMPROVE SURVIVAL IN GLIOBLASTOMA? A META-ANALYSIS OF RANDOMIZED TRIALS IN NEWLY DIAGNOSED GLIOBLASTOMA
Bibliographic record
Abstract
Several small uncontrolled retrospective case series as well as a post-hoc analysis of the registration trial for temozolomide in newly diagnosed glioblastoma (Weller et al., 2011) have indicated an association between valproic acid (VPA) use and improved outcome in patients with newly diagnosed glioblastoma. To confirm the hypothesis generated based on the analysis of the temozolomide registration trial, we performed a combined analysis of a survival association of AED use at the start of chemoradiotherapy with temozolomide in the pooled patient cohort (n = 1869) of four contemporary randomized clinical trials in newly diagnosed glioblastoma: AVAGlio (NCT00943826), RTOG-0825 (NCT00884741), CENTRIC (NCT00689221) and CORE (NCT00813943). Progression-free (PFS) and overall survival (OS) were compared between (i) VPA versus no AED, (ii) VPA versus enzyme-inducing (EI)-AED, or (iii) VPA versus other non-EI-AED (without VPA). Results of Cox regression models stratified by trial and adjusted baseline prognostic factors including O6-methylguanine DNA methyltransferase (MGMT) promoter methylation status were interpreted. The same analyses were performed with levetiracetam (LEV). VPA use at the start of chemoradiotherapy was not associated with improved PFS or OS compared with patients receiving no AED (PFS: hazard ratio (HR) = 0.92, 95% confidence interval (CI) 0.75-1.13, p = 0.41; OS: HR = 1.00, 95% CI 0.80-1.25, p = 0.95), EI-AED (PFS: HR = 0.95, 95% CI 0.74-1.21, p = 0.62; OS: HR = 1.02, 95% CI 0.0.77-1.33, p = 0.93) or non-EI-AED (PFS: HR = 1.02, 95% CI 0.80-1.3, p = 0.92; OS: HR = 1.06, 95% CI 0.83-1.35, p = 0.67). Similarly, no association with outcome was seen for LEV use. This pooled analysis did not validate an association of VPA or LEV use with improved survival, challenging the need for a full phase III trial exploring the repurposing of VPA or LEV as add-on to the standard of care treatment of newly diagnosed glioblastoma.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.032 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".