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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 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)0.002
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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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.

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; 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 designNon-randomized trial
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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