Frontline Management of Nodal Peripheral T-Cell Lymphomas
Bibliographic record
Abstract
Peripheral T-cell lymphomas (PTCLs) represent only 10%-15% of all non-Hodgkin lymphoma but encompass a diverse group of diseases with over 30 different subtypes. As a result of both disease heterogeneity and rarity, therapeutic progress of PTCLs has lagged behind B-cell lymphomas with very few randomized controlled studies to guide management. The most common subtypes are the so-called nodal PTCLs: PTCL-not otherwise specified (NOS), anaplastic large cell lymphoma (ALCL), and nodal T follicular helper cell lymphoma (TFHL) lymphoma, the latter of which includes angioimmunoblastic T-cell lymphoma. Anthracycline-based primary chemotherapy is still the mainstay of treatment for these common PTCL subtypes, but in recent years, we have moved into an era where more personalized therapy can be applied in some settings. Cyclophosphamide, doxorubicin, prednisone, and brentuximab vedotin CHP-BV is the first therapy in PTCL to show an overall survival benefit and represents a new standard for ALCL; however, there is less therapeutic certainty in other CD30-positive PTCLs. Recurrent mutations of epigenetic modifier genes typify TFHLs lymphomas, and collective studies demonstrate a heightened sensitivity to epigenetic therapies, leading to trials integrating these agents in the frontline setting. Molecular studies of PTCL-NOS have defined at least two subtypes, GATA3 and TBX21, the former having a poorer prognosis, but how this guides therapeutics remains unknown. Outside of ALCL, there is a growing debate as to whether trials should focus on adding a novel agent to cyclophosphamide, doxorubicin, vincristine, and prednisone (CHOP) or whether combination novel therapies should be explored in the frontline therapy setting. Finally, the role of consolidative autologous stem-cell transplant in first remission remains an area of active debate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".