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The Use Of GDP (Gemcitabine, Dexamethasone and Cisplatin) in The Primary Therapy Of Peripheral T-Cell Lymphomas

2013· article· en· W14419551 on OpenAlexaffabout
Jean‐Michel Lavoie, Joseph M. Connors, Diego Villa, Richard Klasa, Tamara Shenkier, Randy D. Gascoyne, Alina S. Gerrie, Laurie H. Sehn, Kerry J. Savage

Bibliographic record

VenueBlood · 2013
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsMedicineInternal medicineCHOPGemcitabineOncologyChemotherapy regimenAnaplastic large-cell lymphomaPopulationLymphomaChemotherapyGastroenterology

Abstract

fetched live from OpenAlex

Abstract Introduction The outcome of peripheral T-cell lymphomas (PTCLs) is poor using standard CHOP chemotherapy. Previous studies have shown good single agent activity of gemcitabine in the relapsed/refractory setting. GDP (gemcitabine, dexamethasone and cisplatin) was developed as a secondary chemotherapy regimen in relapsed aggressive lymphomas and in a comparison to DHAP has recently been shown to have equivalent efficacy, a favourable side effect profile, and it can be delivered in the outpatient setting. At the BC Cancer Agency, GDP has recently been integrated into the primary therapy of patients with PTCLs in an attempt to improve outcomes with CHOP chemotherapy in this poor risk population. Methods The BC Cancer Agency Centre for Lymphoid Cancer and pharmacy databases were searched to identify all cases of newly diagnosed PTCLs with diagnoses by the WHO classification, (with the exception of ALK-positive ALCL, extranodal NK/T-cell lymphoma, cutaneous T-cell lymphoma and hepatosplenic t-cell lymphoma) that received at least one cycle of GDP chemotherapy integrated into their primary therapy, typically alternating with CHOP. Results In total, 34 patients received GDP as part of their first-line treatment (PTCL-NOS n=19, ALK-neg ALCL n=10, enteropathy-type TCL n=2, angioimmunoblastic T-cell lymphoma n=3) in addition to CHOP chemotherapy. The median age was 58 years, 65% were male and the majority of patients had high risk features including stage 3 or 4 disease (94%), elevated LDH (62%). high IPI score (>3 65%; >2 82%) and 32% had bone marrow involvement. The median number of cycles of GDP was 3 (1-8). Two patients underwent consolidative treatment with high dose chemotherapy and autologous stem cell transplantation in first remission and two patients received consolidative radiotherapy. The overall response rate at the end of primary chemotherapy was 82% (CR 62%). With a median follow-up in living patients of 2.8 years the 1- and 2-year time to progression (TTP) were 50% and 36%, respectively and the corresponding estimates for OS were 78% and 64%. Interestingly, the IPI was not prognostic (2 y TTP IPI 0,1 42%, 2,3 34%%, 4,5 37.5% p=.87) even if the 2 patients undergoing transplant are excluded. GDP was generally well tolerated and only 2 patients discontinued treatment due to toxicity (renal dysfunction/hearing loss and rash) and there were no treatment-related deaths. Two patients were hospitalized for febrile neutropenia following GDP and 9 patients (26%) required GCSF support through their therapy. Conclusion The use of GDP in the primary treatment of PTCL is associated with a high response rate. Outcomes in high IPI patients compare favourably with historical results using CHOP chemotherapy and suggest that it may be able to overcome disease resistance in this poor risk group, providing rationale to explore it in further in future clinical trials of PTCLs. Disclosures: Connors: F Hoffmann-La Roche: Research Funding; Roche Canada: Research Funding. Savage:Eli-Lilly: Consultancy.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.229
Teacher spread0.203 · 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 designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2013
Admission routes2
Has abstractyes

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