Diffuse large B-cell lymphoma: what clinical progress have we seen in the last 5 years?
Bibliographic record
Abstract
INTRODUCTION: Diffuse large B-cell lymphoma is the most common subtype of non-Hodgkin lymphoma and has a rapidly evolving treatment landscape. For patients at increased risk of primary treatment failure and those with relapsed/refractory disease, emerging therapeutic classes have significantly improved outcomes. AREAS COVERED: This review describes the most important trials evaluating treatment for DLBCL in frontline and the relapsed/refractory setting for both fit patients and patients ineligible for intensive therapy. Particular attention is paid to rational treatment sequencing and selection for CAR-T, bispecific antibodies and molecularly targeted therapies. EXPERT OPINION: In modern DLBCL therapy, it is critical to identify individuals at increased risk of primary treatment failure with R-CHOP, who may then be offered the addition of Polatuzumab Vedotin or other targeted therapies in frontline. CAR-T is now a treatment standard in second line for patients with primary refractory or early relapsed disease, with ASCT reserved for eligible patients with later relapse, who may subsequently receive CAR-T in third-line. ASCT ineligible patients also face improved outcomes with CAR-T and enhanced therapies incorporating novel agents. Molecular and genomic tumor profiling is likely in the future to direct optimal treatment for patients by identifying biologically distinct lymphomas sensitive to distinct targeted agents.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".