Clinical impact of molecular features in diffuse large B-cell lymphoma and follicular lymphoma
Bibliographic record
Abstract
Our understanding of the pathogenesis and heterogeneity of diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) has been dramatically enhanced by recent attempts to profile molecular features of these lymphomas. In this article, we discuss ways in which testing for molecular features may impact DLBCL and FL management if clinical trials are designed to incorporate such tests. Specifically, we discuss how distinguishing lymphomas on the basis of cell-of-origin subtypes or the presence of other molecular features is prognostically and therapeutically significant. Conversely, we discuss how the molecular similarities of DLBCL and FL have provided insight into the potential of both DLBCL and FL cases to respond to agents targeting alterations they have in common. Through these examples, we demonstrate how the translation of our understanding of cancer biology into improvements in patient outcomes depends on analyzing the molecular correlates of treatment outcomes in clinical trials and in routinely treated patients.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".