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Record W4289836925 · doi:10.1038/s41408-022-00710-9

Venetoclax ex vivo functional profiling predicts improved progression-free survival

2022· letter· en· W4289836925 on OpenAlexaff
Vikas A. Gupta, Shannon M. Matulis, Benjamin G. Barwick, R. Devin Bog, Conrad W. Shebelut, Mala Shanmugam, Paola Neri, Nizar J. Bahlis, Madhav V. Dhodapkar, Leonard T. Heffner, Craig C. Hofmeister, Nisha S. Joseph, Sagar Lonial, Jonathan L. Kaufman, David L. Jaye, Ajay K. Nooka, Lawrence Boise

Bibliographic record

VenueBlood Cancer Journal · 2022
Typeletter
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsInstitute of Cancer ResearchUniversity of Calgary
FundersWinship Cancer InstituteEmory UniversityMultiple Myeloma Research FoundationNational Cancer InstituteU.S. Department of Health and Human ServicesNational Institutes of HealthAmerican Cancer Society
KeywordsEx vivoVenetoclaxProfiling (computer programming)In vivoMedicineOncologyComputational biologyInternal medicineBiologyBioinformaticsCancer researchLeukemiaComputer scienceGenetics

Abstract

fetched live from OpenAlex

In multiple myeloma, the t(11;14) translocation enriches for patients likely to respond to the Bcl2 inhibitor venetoclax. In this group of patients, 40% respond to single-agent venetoclax while up to 60% respond to the combination of venetoclax and dexamethasone [ 1 , 2 ]. We have previously demonstrated that ex vivo functional profiling of venetoclax sensitivity can more accurately identify these venetoclax-responsive patients [ 3 ]. Here we report updated data on a larger cohort of patients who underwent ex vivo testing and were subsequently treated with venetoclax. We demonstrate that this 24-hour functional assay can rapidly predict patient responses to venetoclax that translate into improved progression-free survival (PFS).

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.039
GPT teacher head0.320
Teacher spread0.281 · 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".

Quick stats

Citations9
Published2022
Admission routes1
Has abstractno

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