Adjuvant everolimus in high-risk diffuse large B-cell lymphoma: final results from the PILLAR-2 randomized phase III trial
Bibliographic record
Abstract
Background: Patients with diffuse large B-cell lymphoma (DLBCL) with an International Prognostic Index (IPI) ≥3 are at higher risk for relapse after a complete response (CR) to first-line rituximab-based chemotherapy (R-chemo). Everolimus has single-agent activity in lymphoma. PILLAR-2 aimed to improve disease-free survival (DFS) with 1 year of adjuvant everolimus. Patients and methods: Patients with high-risk (IPI ≥3) DLBCL and a positron emission tomography/computed tomography-confirmed CR to first-line R-chemo were randomized to 1 year of everolimus 10 mg/day or placebo. The primary end point was DFS; secondary end points were overall survival, lymphoma-specific survival, and safety. Results: Between August 2009 and December 2013, 742 patients were randomized to everolimus (n = 372) or placebo (n = 370). Median follow-up was 50.4 months (range 24.0-76.9). Overall, 47% of patients were ≥65 years, 50% were male, and 42% had an IPI of 4 or 5. 48% and 67% completed everolimus and placebo, respectively. Primary reasons for everolimus discontinuation versus placebo were adverse events (AEs; 30% versus 12%) and relapsed disease (6% versus 13%). Everolimus did not significantly improve DFS compared with placebo (hazard ratio 0.92; 95% CI 0.69-1.22; P = 0.276). Two-year DFS rate was 77.8% (95% CI 72.7-82.1) with everolimus and 77.0% (95% CI 72.1-81.1) with placebo. Common grade 3/4 AEs with everolimus were neutropenia, stomatitis, and decreased CD4 lymphocytes. Conclusions: Adjuvant everolimus did not improve DFS in patients already in PET/CT-confirmed CR. Future approaches should incorporate targeted agents such as everolimus with R-CHOP rather than as adjuvant therapy after CR has been obtained. ClinicalTrials.gov: NCT00790036.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".