Bibliographic record
Abstract
PURPOSE OF REVIEW: Diffuse large B cell lymphoma (DLBCL) is the most common lymphoma subtype, characterized by marked clinical and biologic heterogeneity. Gene expression studies together with new monoclonal antibody production are playing an increasing role in determining important prognostic factors/biomarkers predictive of outcome. Despite these technical advances, much confusion exists in the literature as to what constitutes the important biomarkers for determining patient outcome. The purpose of this review is to highlight recent advances in our understanding of novel biomarkers in DLBCL and how these might be incorporated into current risk-adjustment models for prognosis. RECENT FINDINGS: Microarray gene expression analyses have revolutionized our approach to biomarkers in non-Hodgkin lymphomas. Thousands of genes can now be simultaneously analyzed for individual patients, creating a wealth of new data. This has resulted in an improved understanding of the basic biology, as well as the development of new outcome predictors. Monoclonal antibody reagents for some of these biomarkers already exist, allowing for their rapid validation at the level of protein expression and potential clinical translation. SUMMARY: A molecular classification of DLBCL is a current reality, and together with routine morphology, immunophenotype, and molecular cytogenetics, has allowed us to more accurately subclassify DLBCL and determine clinically relevant subgroups. The time is right to begin to consider how these novel biomarkers should be incorporated into current prognostic models to move beyond the clinically based International Prognostic Index
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".