ACTR-57. DOES THE ADDITION OF VALPROIC ACID TO CONCURRENT RADIATION THERAPY AND TEMOZOLOMIDE IMPROVE PATIENT OUTCOME? – CORRELATIVE ANALYSIS OF RTOG 0525, SEER AND A PHASE II NCI TRIAL
Bibliographic record
Abstract
The addition of the antiepileptic agent valproic acid (VPA) to standard radiation therapy (RT) and temozolomide (TMZ) was evaluated in a two center, open-label, phase II study (NCI-06-C-0112). The intent of this current analysis was to compare patient outcome with modern era standard of care data (RTOG 0525) and general population data (SEER 2006–2013). 37 GBM patients were treated in a phase II NCI trial with daily VPA (25 mg/kg) in addition to concurrent RT and TMZ (2006–2013). 411 GBM patients were treated in standard TMZ dose arm of RTOG 0525 (2006–2008). Using the SEER database (2006–2013), adult patients with GBM were identified and 6402 were included in the analysis. Kaplan-Meier method was used to estimate OS and PFS. The effect of patient characteristics and clinical factors on OS and PFS was analyzed using univariate analysis, a Cox regression model and landmark analysis. Median OS in the NCI cohort was superior to the RTOG 0525 and SEER cohorts with a median OS of 29.6 months (21- 63.8), compared to 18.9 months (16.8–20.3) and 13.1 months (p= 0.007). Median PFS was superior in the NCI cohort, 10.2 months (6.6 – 49.6) as compared to RTOG 0525 with a median PFS of 7.5 months (6.9–8.2) (p = 004). In comparison to RTOG 0525, the population in the NCI cohort had better KPS and RPA, and a higher proportion of patients receiving bevacizumab (57% vs 28%), however with the exception of RPA (V), the effects of these factors on PFS and OS were not significantly different between the two cohorts. Previously reported improvements in PFS and OS with the addition of VPA to concurrent RT and TMZ in the NCI phase II study were confirmed in comparison to RTOG0525 and a contemporary SEER cohort.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.002 | 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".