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Record W3033465546 · doi:10.1111/bjh.16777

Validation of the RHL30 digital gene expression assay as a prognostic biomarker for relapsed Hodgkin lymphoma

2020· article· en· W3033465546 on OpenAlexafffund
Lourdes Calvente, Rosemarie Tremblay‐LeMay, Wei Xu, Fong Chun Chan, Michael Hong, Tong Zhang, Ho‐Young Yhim, John Kuruvilla, Michael Crump, Vishal Kukreti, Anca Prica, Dean A. Regier, Marco A. Marra, Aly Karsan, Christian Steidl, David W. Scott, Peter Sabatini, Robert Kridel

Bibliographic record

VenueBritish Journal of Haematology · 2020
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsUniversity of TorontoCanada's Michael Smith Genome Sciences CentrePrincess Margaret Cancer CentreSpinal Cord Injury BCUniversity Health Network
FundersPrincess Margaret Cancer FoundationGenome Canada
KeywordsMedicineBiomarkerOncologyLymphomaInternal medicineTransplantationCohortAutologous stem-cell transplantationBiology

Abstract

fetched live from OpenAlex

Despite continuing improvements in the management of classical Hodgkin lymphoma (cHL), relapse remains associated with a risk of lymphoma-related mortality. The biological composition of relapse tumour biopsies shows interpatient variability, which can be leveraged to design prognostic biomarkers. Here, we validated the RHL30 assay, a previously reported gene expression model in an independent cohort of 41 patients with relapsed cHL. Patients classified as high-risk by the RHL30 assay had inferior failure-free survival (FFS) after autologous stem cell transplantation (2-year FFS 41% vs. 92%, P = 0·035). The RHL30 model is a robust biomarker that risk-stratifies patients considered for autologous stem cell transplantation.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.266
Teacher spread0.243 · 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

Citations10
Published2020
Admission routes2
Has abstractyes

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