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Record W2059078891 · doi:10.1097/pap.0b013e3182169948

MYC and Aggressive B-cell Lymphomas

2011· review· en· W2059078891 on OpenAlexaff
Graham W. Slack, Randy D. Gascoyne

Bibliographic record

VenueAdvances in Anatomic Pathology · 2011
Typereview
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsLymphomaFollicular lymphomaCancer researchAggressive lymphomaGene rearrangementB cellMedicineLymphoblastic lymphomaOncogeneLeukemiaPathologyBiologyCell cycleGeneImmunologyCancerT cellInternal medicineGeneticsAntibody

Abstract

fetched live from OpenAlex

Rearrangement of the proto-oncogene MYC leads to MYC protein deregulation and is an important driver of oncogenic transformation. MYC rearrangement is a recurring genetic abnormality in several aggressive B-cell lymphomas including: Burkitt lymphoma, diffuse large B-cell lymphoma; B-cell lymphoma, unclassifiable with features intermediate between diffuse large B-cell lymphoma and Burkitt lymphoma; rare de novo acute lymphoblastic lymphoma/leukemia, transformed follicular lymphoma, and plasmablastic lymphoma. Important distinctions in the role of MYC in these tumors likely reflect whether it is a primary or secondary genetic event. The presence of a MYC rearrangement in these diseases has diagnostic and prognostic implications and it is important for the practicing anatomic pathologist to be familiar with these issues when diagnosing aggressive B-cell lymphomas. This review provides a brief overview of MYC biology; shows the clinical and pathologic features of the aggressive B-cell lymphomas that harbor recurrent MYC rearrangements; explores the diagnostic and clinical implications of MYC rearrangements in these diseases; and outlines the techniques available to the anatomic pathologist to detect MYC deregulation.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.342
Teacher spread0.317 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations137
Published2011
Admission routes1
Has abstractyes

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