Diffuse large B-cell lymphoma: optimizing outcome in the context of clinical and biologic heterogeneity
Bibliographic record
Abstract
Although the majority of patients with diffuse large B-cell lymphoma (DLBCL) can be cured with standard rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP), patients who fail R-CHOP have a dismal outcome. Thus, optimization of front-line therapy, as well as the development of more effective salvage strategies, remains an important objective. Advances in molecular genetics have vastly improved our understanding of the biological diversity of DLBCL and have led to the discovery of key oncogenic pathways. In addition to the major molecular designations of germinal center B-cell and activated B-cell subtypes, next-generation sequencing technologies have unveiled the remarkable complexity of DLBCL and identified unique molecular targets that may be differentially exploited for therapeutic benefit. These findings have translated into a growing list of promising novel agents. Moving forward, it is of paramount importance to recognize the heterogeneity of DLBCL and to investigate these targeted agents within patient populations who are most likely to benefit. It will be necessary to prioritize drugs that affect key driver pathways and to combine them rationally to optimize their benefit. Improved prognostication and the availability of predictive biomarkers will be crucial to allow for the possibility of individualized risk-adapted therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".