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Record W2460247604 · doi:10.1080/10428194.2016.1193852

Gemcitabine, dexamethasone, and cisplatin (GDP) is an effective and well-tolerated salvage therapy for relapsed/refractory diffuse large B-cell lymphoma and Hodgkin lymphoma

2016· article· en· W2460247604 on OpenAlexaff
Alden A. Moccia, Felicitas Hitz, Paul Hoskins, Richard Klasa, Maryse Power, Kerry J. Savage, Tamara Shenkier, John D. Shepherd, Graham W. Slack, Kevin Song, Randy D. Gascoyne, Joseph M. Connors, Laurie H. Sehn

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2016
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
Fundersnot available
KeywordsGemcitabineSalvage therapyMedicineInternal medicineRegimenOncologyDiffuse large B-cell lymphomaRefractory (planetary science)LymphomaDexamethasoneChemotherapySurgeryBiology

Abstract

fetched live from OpenAlex

The optimal choice of salvage therapy for patients with relapsed/refractory diffuse large B-cell lymphoma (DLBCL) or Hodgkin lymphoma (HL) remains unknown. Based on promising results of phase II trials, the preferred salvage regimen in British Columbia since 2002 has been the out-patient regimen, gemcitabine, dexamethasone, and cisplatin (GDP). We conducted a retrospective analysis including all patients with relapsed/refractory DLBCL or HL who received GDP as salvage therapy between September 2002 and June 2010. We identified 235 patients: 152 DLBCL, 83 HL. Overall response rates were 49% and 71% for patients with DLBCL and HL, respectively. Within the transplant-eligible population, 52% of patients with DLBCL and 96% of patients with HL proceeded to stem cell transplantation. The 2-year progression-free survival and overall survival were 21% and 28% in the DLBCL cohort, and 58% and 85% in the HL group. GDP is an effective and well-tolerated out-patient salvage regimen for relapsed/refractory DLBCL and HL.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.246
Teacher spread0.236 · 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; both teacher heads agree on what is shown here.

Study designOther design
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".

Quick stats

Citations47
Published2016
Admission routes1
Has abstractyes

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