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Record W3211610920 · doi:10.1007/s10014-021-00415-0

Central nervous system ALK-negative anaplastic large cell lymphoma with IRF4/DUSP22 rearrangement

2021· article· en· W3211610920 on OpenAlexaff
Shino Magaki, Radha Satyadev, Zesheng Chen, Kathryn S. Yung, Harry V. Vinters, Marsha C. Kinney, Jonathan W. Said

Bibliographic record

VenueBrain Tumor Pathology · 2021
Typearticle
Languageen
FieldMedicine
TopicCNS Lymphoma Diagnosis and Treatment
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsAnaplastic large-cell lymphomaPathologyFluorescence in situ hybridizationPrimary central nervous system lymphomaAnaplastic lymphoma kinaseGene rearrangementCorpus callosumMedicineBiologyLymphomaGeneticsGene

Abstract

fetched live from OpenAlex

Anaplastic large cell lymphomas (ALCL) are mature T-cell neoplasms, approximately half of which harbor rearrangements of the ALK gene that confer a good prognosis. Recent studies have demonstrated that a significant proportion of ALK-negative ALCLs demonstrate rearrangements of the IRF4/DUSP22 locus that also are typically associated with a favorable prognosis. ALCL with primary involvement of the central nervous system (CNS) is extremely rare. We report what may be the first case of ALK-negative ALCL with IRF4/DUSP22 rearrangement involving the brain in a 55-year-old man. Magnetic resonance imaging demonstrated signal abnormalities in the periventricular region, corpus callosum and cingulate gyrus. Biopsy revealed a diffuse parenchymal and angiocentric infiltrate of CD30-positive cells that showed IRF4/DUSP22 rearrangement by fluorescence in situ hybridization. We also review the clinical and pathologic features of primary CNS ALK-negative ALCLs in the literature and highlight the need for awareness of this entity to optimize appropriate management.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.007
GPT teacher head0.221
Teacher spread0.214 · 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 designCase report
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

Citations6
Published2021
Admission routes1
Has abstractyes

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