Aggressive Peripheral T-Cell Lymphomas (Specified and Unspecified Types)
Bibliographic record
Abstract
Peripheral T-cell lymphomas (PTCLs) are a biologically diverse and uncommon group of diseases. Compared to their B-cell counterparts, PTCLs remain largely unexplored and the optimal treatment ill-defined due to disease rarity and biological heterogeneity. For the majority of PTCL subtypes, prognosis is poor with a 5-year overall survival of approximately 30% in most series.The notable exception is ALK-positive anaplastic large-cell lymphoma (ALK-pos ALCL), which has a superior outcome. The international prognostic index can be used to some extent to define risk groups within some PTCL subtypes, including PTCL unspecified (PTCLUS). It is likely that the observed clinical heterogeneity reflects differences at the molecular level. With the more widespread availability of gene expression profiling, it may be possible in the future to further refine the classification of PTCLs and elucidate novel therapeutic targets. Future clinical trials are needed that focus specifically on PTCL to advance our understanding and define the optimal management in this disease.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".