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Record W4283733194 · doi:10.3390/curroncol29070363

Concurrent Waldenstrom’s Macroglobulinemia and Myelodysplastic Syndrome with a Sequent t(10;13)(p13;q22) Translocation

2022· article· en· W4283733194 on OpenAlexvenueno aff
Peter DeRosa, Kyle C. Roche, Victor E. Nava, Sunita Singh, Minling Liu, Anita Agarwal

Bibliographic record

VenueCurrent Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsnot available
Fundersnot available
KeywordsWaldenstrom macroglobulinemiaChromosomal translocationMedicineCancer researchMacroglobulinemiaMyeloidBone marrowMyelopoiesisLymphoplasmacytic LymphomaMyelodysplastic syndromesHaematopoiesisImmunologyBiologyLymphomaMultiple myelomaGeneticsStem cellGene

Abstract

fetched live from OpenAlex

Myelodysplastic syndromes (MDS) and Waldenstrom’s macroglobulinemia (WM) are rarely synchronous. Ineffective myelopoiesis/hematopoiesis with clonal unilineage or multilineage dysplasia and cytopenias characterize MDS. Despite a myeloid origin, MDS can sometimes lead to decreased production, abnormal apoptosis or dysmaturation of B cells, and the development of lymphoma. WM includes bone marrow involvement by lymphoplasmacytic lymphoma (LPL) secreting monoclonal immunoglobulin M (IgM) with somatic mutation (L265P) of myeloid differentiation primary response 88 gene (MYD88) in 80–90%, or various mutations of C-terminal domain of the C-X-C chemokine receptor type 4 (CXCR4) gene in 20–40% of cases. A unique, progressive case of concurrent MDS and WM with several somatic mutations (some unreported before) and a novel balanced reciprocal translocation between chromosomes 10 and 13 is presented below.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.067
GPT teacher head0.376
Teacher spread0.309 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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