The Effect of Levetiracetam Treatment on Survival in Patients with Glioblastoma: A Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
Abstract Background:Levetiracetam (LEV) is an anti-epileptic drug (AED) that sensitizes glioblastoma (GBM) to temozolomide (TMZ) chemotherapy by inhibiting O6-methylguanine-DNA methyltransferase (MGMT) expression. Adding LEV to the standard of care (SOC) for GBM may improve TMZ efficacy. This study aimed to pool the existing evidence in the literature to quantify LEV’s effect on GBM survival and characterize its safety profile to determine whether incorporating LEV into the SOC is warranted.Method:A search of CINAHL, Embase, PubMed, and Web of Science from inception to May 2021 was performed to identify relevant articles. Hazard ratios (HR), median overall survival, and adverse events were pooled using random-effect models. Meta-regression, funnel plots, and the Newcastle-Ottawa Scale were utilized to identify sources of heterogeneity, bias, and statistical influence.Results:From 20 included studies, 5804 GBM patients underwent meta-analysis, of which 1923 (33%) were treated with LEV. Administration of LEV did not significantly improve survival in the entire patient population (HR=0.89, p=0.094). Significant heterogeneity was observed during pooling of HRs (I2=75%, p<0.01). Meta-regression determined that LEV treatment effect decreased with greater rates of MGMT methylation (RC=0.03, p=0.02) and increased with greater proportions of female patients (RC=-0.05, p=0.002). Concurrent LEV with the SOC for GBM did not increase odds of adverse events relative to other AEDs.Conclusions:Levetiracetam treatment may not be effective for all GBM patients. Instead, LEV may be better suited for treating specific molecular profiles of GBM. Further studies are necessary to identify optimal GBM candidates for LEV.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.043 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".