Paramount prognostic factors that guide therapeutic strategies in diffuse large B-cell lymphoma
Bibliographic record
Abstract
Abstract Outcome in diffuse large B-cell lymphoma (DLBCL) has improved over the last decade and will likely improve further with the introduction of novel agents. At the present time, clinical prognostic factors are limited in their ability to identify patients with sufficiently poor outcome to justify deviation of therapy away from R-CHOP (rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone) outside of a clinical trial. Similarly, with the exception of the concurrent translocation of MYC and BCL2, there are no validated biologic markers that can be used to guide initial therapy in routine practice. Recognition of the molecular heterogeneity of DLBCL is of paramount importance and must be taken into consideration when investigating new therapies. It will be vital for novel targeted agents to be evaluated in patient populations enriched for those who are most likely to benefit. The identification of prognostic and predictive biomarkers should be initiated during the early phase of drug development so that these tests can be validated within phase 3 trials. Although currently available techniques such as immunohistochemistry may still be used, gene-expression profiling and whole genomic analytic techniques will likely play a major role in the evaluation of patients in the future to determine optimal personalized treatment for DLBCL.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".