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Record W4403738904 · doi:10.1080/10428194.2024.2416577

Matching-adjusted indirect comparisons of zanubrutinib (MAGNOLIA, BGB-3111-AU-003) versus ibrutinib (PCYC-1121) and rituximab (CHRONOS-3) in relapsed/refractory marginal zone lymphoma

2024· article· en· W4403738904 on OpenAlexaff
Catherine Thieblemont, Björn E. Wahlin, Leyla Mohseninejad, Kaijun Wang, Ina Zhang, Sam Keeping, Keri Yang, Pier Luigi Zinzani

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2024
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsPrecision Nanosystems (Canada)
FundersBeiGene
KeywordsIbrutinibRituximabRefractory (planetary science)MedicineLymphomaInternal medicineLeukemiaBiology

Abstract

fetched live from OpenAlex

In the absence of head-to-head randomized trials, unanchored matching-adjusted indirect comparisons were conducted to estimate the relative efficacy of zanubrutinib versus ibrutinib and zanubrutinib versus rituximab in relapsed or refractory marginal zone lymphoma (MZL). Logistic propensity score models were used to estimate weights for the patient-level data from two phase II single-arm trials, MAGNOLIA and BGB-3111-AU-003, such that their characteristics matched the ibrutinib and rituximab aggregate-level data from PCYC-1121 and CHRONOS-3, respectively. The base case model for each comparison incorporated four key prognostic factors: prior lines of therapy, MZL subtype, response to prior therapy, and age. A sensitivity analysis incorporating additional prognostic factors was also conducted for the ibrutinib comparison. The impact of each covariate was explored via a leave-one-out analysis. Compared with ibrutinib and rituximab, zanubrutinib demonstrated significant benefits in terms of both overall response and progression-free survival in patients with previously treated MZL.

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)0.002
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 score0.803
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.003
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.025
GPT teacher head0.289
Teacher spread0.264 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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