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Therapies for Peripheral T-Cell Lymphomas

2011· review· en· W2137905169 on OpenAlexaff
Kerry J. Savage

Bibliographic record

VenueHematology · 2011
Typereview
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsVincristineMedicineCHOPAnaplastic large-cell lymphomaOncologyLymphomaInternal medicinePrednisoneBrentuximab vedotinRegimenCyclophosphamideDoxorubicinChemotherapyCD30

Abstract

fetched live from OpenAlex

Peripheral T-cell lymphomas (PTCLs) are a rare and heterogeneous group of disorders that, for the most part, are associated with a very poor prognosis. The standard therapy for PTCLs is CHOP (cyclophosphamide, doxorubicin, vincristine, and prednisone) or a comparable CHOP-like regimen that incorporates anthracyclines. With the exception of anaplastic lymphoma kinase-positive anaplastic large cell lymphoma (ALK(+) ALCL), the cure rate for PTCLs with CHOP is low, and limited evidence suggests that anthracyclines do not improve the prognosis. However, there is no compelling evidence that any other regimen or approach is superior. It remains challenging to compare alternative therapies or treatment strategies with CHOP because the majority of data are retrospective and include diverse patient populations. Recently, prospective studies have been initiated exclusively for PTCL, and in some, select histologic subtypes are evaluated in an effort to remove heterogeneity. Encouragingly, there have been several new therapies emerging with activity in PTCLs and exciting novel combinations under consideration that will hopefully move the field forward and improve outcome in this challenging group of diseases.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.003

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.

Opus teacher head0.077
GPT teacher head0.347
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations48
Published2011
Admission routes1
Has abstractyes

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