Prognostic biomarkers in malignant lymphomas
Bibliographic record
Abstract
There has recently been a rapid expansion in research aimed at identifying biomarkers that could improve the prognosis for patients with various subtypes of malignant lymphoma. Genomic and genetic studies have led to the identification of biological and clinical subgroups of diffuse large B-cell lymphomas with distinct underlying molecular features, divergent activation of oncogenetic pathways, and clinical course. Molecular studies of follicular lymphoma have suggested complex interactions between malignant cells and the surrounding immunological network that could affect disease progression. Moreover, the inflammatory cells of Hodgkin lymphoma have been shown to produce a complex network of cytokines and chemokines that provide a permissive microenvironment for tumor growth. Research into specific biomarkers and signaling pathways of malignant lymphomas might therefore result in the identification of novel targets for future therapeutic strategies. As gene expression profiling techniques are not yet feasible in the clinical laboratory, studies have aimed to translate the findings into more widely applicable techniques that might allow this research to be applied to routine clinical practice. This review focuses on recent advances in translational and clinical research on biomarkers in malignant lymphomas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 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 | 0.001 |
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".