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Record W3170616394 · doi:10.1038/s41375-021-01314-1

Gene expression-based outcome prediction in advanced stage classical Hodgkin lymphoma treated with BEACOPP

2021· letter· en· W3170616394 on OpenAlexafffund
Ron D. Jachimowicz, Wolfgang Hiddemann, Gunther Glehr, Horst Müller, Heinz Haverkamp, Christoph Thorns, Martin L. Hansmann, Peter Mӧller, Harald Stein, Thorsten Rehberg, Bastian von Tresckow, Hans Christian Reinhardt, Peter Borchmann, Fong Chun Chan, Rainer Spang, David W. Scott, Andreas Engert, Christian Steidl, Michael Altenbuchinger, Andreas Rosenwald

Bibliographic record

VenueLeukemia · 2021
Typeletter
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsBC Cancer Agency
FundersCanadian Institutes of Health ResearchBC Cancer FoundationMax-Planck-GesellschaftMichael Smith Health Research BC
KeywordsLymphomaClassical Hodgkin lymphomaStage (stratigraphy)OncologyOutcome (game theory)Internal medicineMedicineHodgkin lymphomaGene expressionCancer researchGeneBiologyGeneticsMathematics

Abstract

fetched live from OpenAlex

Classical Hodgkin Lymphoma (cHL) is a B cell-derived lymphoid malignancy, affecting 2.5-3/100,000 people per year. To date, in patients diagnosed with advanced cHL no reliable tool is able to-a priori-distinguish the subset of patients at high risk for relapse or refractory disease. Clinical risk indices for cHL, such as the International Prognostic Score (IPS), have not been successfully applied as a treatment decision tool in advanced stage cHL In this study we show, that a previously published gene expression-based predictor in advanced stage cHL patients treated with ABVD [2] does not prove prognostic in 401 BEACOPP-treated advanced stage cHL patients. Using transcriptome profiling, we identified however that three individual genes, PDGFRA, TNFRSF8 (encoding CD30) and CCL17 (encoding TARC), were significantly associated with progression-free survival (PFS) after multiple test correction in the BEACOPP-treated cohort, highlighting the potential of a modified gene expression profiling approach for pre-treatment risk assessment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.020
GPT teacher head0.260
Teacher spread0.240 · 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 teacher head, not a consensus.

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

Citations14
Published2021
Admission routes2
Has abstractyes

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