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Record W2048790475 · doi:10.3109/10428194.2014.985673

Structural genomic alterations in primary mediastinal large B-cell lymphoma

2014· review· en· W2048790475 on OpenAlexafffund
David D. W. Twa, Christian Steidl

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2014
Typereview
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsSpinal Cord Injury BCUniversity of British Columbia
FundersBC Cancer AgencyCanadian Institutes of Health Research
KeywordsLymphomaCIITACancer researchSomatic cellBiologyBreakpointChromothripsisPathogenesisComputational biologyGeneticsGeneGenome instabilityImmunologyDNAChromosomal translocationDNA damage

Abstract

fetched live from OpenAlex

Primary mediastinal large B-cell lymphoma (PMBCL) is an aggressive non-Hodgkin lymphoma that displays phenotypic and genotypic similarity to Hodgkin lymphoma and diffuse large B-cell lymphoma. Studies using genome-wide discovery tools have revealed specific, recurrent structural aberrations as critical somatic events in the pathogenesis of PMBCL. These structural alterations prominently include transcript and protein altering rearrangements and copy number variations of the programmed death ligands 1 (CD274) and 2 (PDCD1LG2), CIITA, JAK2 and REL. Importantly, evidence is emerging that these acquired structural genomic changes, in synergy with other somatic alterations, contribute to PMBCL pathogenesis by influencing tumor microenvironment interactions that favor malignant B-cell growth. The means by which these rearrangements arise are not well understood. However, analysis of breakpoint junctions at base-pair resolution provides preliminary insight into putative rearrangement mechanisms. As the field also anticipates predictive value and therapeutic targeting of structural changes involving programmed death ligands and JAK2, a review of therapies that will likely shape future lymphoma treatment is needed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.017
GPT teacher head0.273
Teacher spread0.256 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations27
Published2014
Admission routes2
Has abstractyes

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