The Evolving Landscape of DLBCL Treatment Beyond the First Line in 2024
Bibliographic record
Abstract
The landscape for treating relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) in 2024 is rapidly evolving, with various treatment options emerging. Traditionally, salvage chemotherapy followed by autologous stem cell transplant (ASCT) has been the primary treatment for young, fit patients with R/R DLBCL, and only limited options exist for those ineligible for transplant. However, recent research and regulatory approvals, such as chimeric antigen receptor (CAR) T-cell and bispecific antibody therapies, have significantly improved our ability to treat patients previously considered palliative for R/R DLBCL. Moreover, further research has demonstrated that these advanced technologies are not only effective in the transplant setting but also in individuals who are not traditionally eligible for ASCT and those with comorbid conditions. One anticipated development has been the provincial approvals of bispecific T-cell engagers (BiTEs), such as epcoritamab and glofitamab, which target CD20 and CD3. BiTE therapy holds promise as an off-the-shelf treatment option, potentially offering wider availability to patients compared to CAR T-celll therapy or even post‑CAR T-cell failure.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".