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Record W4309287965 · doi:10.1080/10428194.2022.2136956

Pembrolizumab for patients with non-Hodgkin lymphoma: phase 1b KEYNOTE-013 study

2022· article· en· W4309287965 on OpenAlexaff
John Kuruvilla, Philippe Armand, Mehdi Hamadani, Justin Kline, Craig H. Moskowitz, David Avigan, Joshua Brody, Vincent Ribrag, Alex F. Herrera, Franck Morschhauser, Abraham S. Kanate, Pier Luigi Zinzani, Jacob D. Bitran, Hervé Ghesquières, Stephen J. Schuster, Mohammed Z.H. Farooqui, Patricia Marinello, Nancy L. Bartlett

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2022
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsPembrolizumabMedicineCohortInternal medicineLenalidomideOncologyLymphomaRefractory (planetary science)Clinical endpointAdverse effectMultiple myelomaCancerClinical trialImmunotherapy

Abstract

fetched live from OpenAlex

The multicohort phase 1b KEYNOTE-013 study (NCT01953692) evaluated the safety and efficacy of pembrolizumab in patients with relapsed or refractory NHL who were ineligible for or failed hematopoietic cell transplantation (HCT). Patients received pembrolizumab (cohort 4) or pembrolizumab plus lenalidomide (cohort 5). Primary end points were safety and objective response rate (ORR) per IWG 2007 criteria. Cohort 4 included 89 patients. ORR was 22% (19/86; 90% CI 15-31; 10 CR, nine PR); ORRs by disease type were 48% (10/21), 10% (2/20), 12% (5/41), and 50% (2/4), for PMBCL, FL, DLBCL, and 'other' NHL, respectively. Toxicity was as predicted. Cohort 5 included 19 patients. ORR was 39% (90% CI 20-61; four CR, three PR). Hematologic toxicities were the most common treatment-related AEs. In conclusion, pembrolizumab following HCT ineligibility/failure confirms prior experience in PMBCL but not with NHL subtypes in this study. Additional analyses in DLBCL may not be warranted.

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), Science and technology studies
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.008
GPT teacher head0.254
Teacher spread0.246 · 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 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".

Quick stats

Citations28
Published2022
Admission routes1
Has abstractyes

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