Peripheral T-cell lymphoma
Bibliographic record
Abstract
Peripheral T-cell lymphomas (PTCLs) are a heterogeneous group of clinically aggressive diseases associated with poor outcome. Studies that focus specifically on PTCL are emerging, with the ultimate goal of improved understanding of disease biology and the development of more effective therapies. However, one of the difficulties in classifying and studying treatment options in clinical trials is the rarity of these subtypes. Various groups have developed lymphoma classifications over the years, including the World Health Organization, which updated its classification in 2008. This article briefly reviews the major lymphoma classification schema, highlights contributions made by the collaborative International PTCL Project, discusses prognostic issues and gene expression profiling, and outlines therapeutic approaches to PTCL. These include the standard chemotherapeutic regimens and other modalities incorporating antifolates, conjugates, histone deacetylase inhibitors, monoclonal antibodies, nucleoside analogs, proteasome inhibitors, and signaling inhibitors. As this review emphasizes, the problem has now evolved into an abundance of drugs and too few patients available to test them. Collaborative groups will aid in future efforts to find the best treatment strategies to improve the outcome for patients with PTCL.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 0.004 |
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".