Emerging Therapies for the Treatment of Relapsed or Refractory Follicular Lymphoma
Bibliographic record
Abstract
With no treatment standard having been established for relapsed and refractory follicular lymphoma, a number of therapeutic approaches are used in Canada. In patients who relapse early or who eventually become resistant to subsequent treatment, prognosis is poor, and new approaches are needed. A number of novel therapies are being examined in this setting, including monoclonal antibodies, immunoconjugates, immunomodulatory agents, and signal transduction inhibitors. With the body of evidence for those emerging therapies accumulating and the standard upfront treatment changing from rituximab and chop (cyclophosphamide-doxorubicin-vincristine-prednisone) or rituximab and cvp (cyclophosphamide-vincristine-prednisone) to bendamustine and rituximab, treatment decisions in the relapsed and refractory setting have become more complex. The choice of subsequent treatment must consider type of upfront treatment; duration of remission; and patient-related factors such as age, comorbidities, and treatment preferences. This paper summarizes the evidence for novel therapies and proposes recommendations for subsequent treatment options by remission duration after induction and maintenance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".