DURABLE BENEFIT OF RITUXIMAB MAINTENANCE POST‐AUTOGRAFT IN PATIENTS WITH RELAPSED FOLLICULAR LYMPHOMA: 12‐YEAR FOLLOW‐UP OF THE EBMT LYMPHOMA WORKING PARTY LYM1 TRIAL
Bibliographic record
Abstract
Purpose: To evaluate the long- term effects of in vivo purging with rituximab 375 mg/m2 weekly x 4(RP) and maintenance rituximab 375 mg/m2 every 2 months for 4 doses (RM) on progression free survival (PFS) in patients with relapsed FL receiving a BEAM autograft (ASCT). Methods: 280 patents with relapsed FL after complete or very good partial remission after salvage chemotherapy were randomly assigned using a factorial design to rituximab (R) purging (RP; 375 mg/m2 once per week for 4 weeks) or observation (NP) before ASCT and to R maintenance (RM; 375 mg/m2 once every 2 months for 4 infusions) or observation (NM) (Pettengell et al. JCO 2014): there is thus a group of patients who received no R (neither for purging nor for maintenance) (no R) and a group who received R both for purging and maintenance (RR). Conclusion: The benefit of R maintenance after ASCT on PFS in patients with chemosensitive relapsed FL is sustained at 12 years, suggesting that RM adds to ASCT-mediated disease eradication and may enhance the curative potential of ASCT, as relapses were rare after 7.5 years. The success of salvage therapies at relapse post ASCT in rituximab naïve patients is reflected in comparable overall survival. Keywords: autologous stem cell transplantation (ASCT); follicular lymphoma (FL); rituximab.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".