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Record W4401437309 · doi:10.1111/bjh.19698

Single gene mutations and prognosis in limited‐stage follicular lymphoma treated with radiation therapy

2024· article· en· W4401437309 on OpenAlexafffund
Samantha Hershenfeld, Josuha W.D. Tobin, Victoria Shelton, Lourdes Calvente, Katherine Lajkosz, Ting Liu, Marianne Brodtkorb, Francesco d’Amore, Maja Ludvigsen, Tara Baetz, David P. LeBrun, Nathalie A. Johnson, Michael Crump, Michael Hong, John Kuruvilla, Rosemarie Tremblay‐LeMay, Michael MacManus, Richard Tsang, David Hodgson, Maher K. Gandhi, Robert Kridel

Bibliographic record

VenueBritish Journal of Haematology · 2024
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsJewish General HospitalPrincess Margaret Cancer CentreQueen's UniversityUniversity Health Network
FundersLeukemia and Lymphoma Society of CanadaLeukemia and Lymphoma Society
KeywordsFollicular lymphomaRadiation therapyOncologyMedicineStage (stratigraphy)Internal medicineLymphomaCohortGeneBioinformaticsBiologyGenetics

Abstract

fetched live from OpenAlex

Radiotherapy is routinely used for management of limited-stage follicular lymphoma (FL), yet half of patients ultimately relapse. We hypothesized that the presence of specific gene mutations may predict outcomes. We performed targeted sequencing of a 69-gene panel in 117 limited-stage FL patients treated with radiotherapy and identified recurrently mutated genes. CREBBP was most frequently mutated, and mutated CREBBP was associated with inferior progression-free survival, though not after false discovery rate adjustment. This association failed to validate in an independent cohort. We conclude that recurrent gene mutations do not predict outcomes in this setting. Alternative biomarkers may offer better prognostic insight.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.254
Teacher spread0.238 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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