Treatment of relapsed and refractory follicular lymphoma: which treatment for which patient for which line of therapy?
Bibliographic record
Abstract
ABSTRACT: Recent advances have transformed the treatment landscape for relapsed and refractory follicular lymphoma. Although chemotherapy has long served as the backbone of treatment, the availability of novel targeted, immunomodulatory, and immunotherapeutic approaches is challenging its relevance. These approaches have focused on targeting epigenetic regulators, components of the B-cell receptor or its downstream intracellular pathways and the follicular lymphoma tumor microenvironment. The recent development of bispecific antibodies and chimeric antigen receptor T-cell therapies, which target both tumor-associated and host-specific antigens, has enabled a redirection of the immune system, enhancing the innate antitumor immune response. Rational combinations of these strategies are actively being evaluated in the relapsed and refractory setting and will inevitably move forward into earlier lines of treatment. The success of these approaches has led to numerous and parallel options for patients and clinicians. The emerging challenge now lies in how best to approach each individual patient with relapsed or refractory follicular lymphoma, addressing complex decision-making that considers a patient's previous treatment history, goals of care, clinical and biological characteristics of recurrence, and personal preferences. Understanding the implications of refractory and transformed disease, as well as the timing and biology of relapse will be critical to support a more personalized treatment approach in the modern era.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".