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Record W2947894993 · doi:10.1097/pas.0000000000001287

Large Cells With CD30 Expression and Hodgkin-like Features in Primary Cutaneous Marginal Zone B-Cell Lymphoma

2019· article· en· W2947894993 on OpenAlexaff
Lucía Prieto‐Torres, Rebeca Manso, Deysy Elisabeth Cieza-Díaz, Margarita Jo, Társila Montenegro-Damaso, Itziar Eraña, Marta Lorda, Dolores Suárez Massa, Salma Machán, Raúl Córdoba, M. Ara, Luís Requena, Socorro Marıá Rodríguez-Pinilla, Miguel Á. Piris

Bibliographic record

VenueThe American Journal of Surgical Pathology · 2019
Typearticle
Languageen
FieldMedicine
TopicCutaneous lymphoproliferative disorders research
Canadian institutionsCanarie
Fundersnot available
KeywordsCD30LymphomaMarginal zonePathologyMedicineDiseaseLarge-cell lymphomaLarge cellHodgkin lymphomaDifferential diagnosisImmunophenotypingB cellAntigenImmunologyInternal medicineAntibodyCancerAdenocarcinoma

Abstract

fetched live from OpenAlex

The presence of CD30 cells in cutaneous lymphomas has come to prominence in recent years as a potential diagnostic and therapeutic marker. In primary cutaneous marginal zone B-cell lymphomas, the presence of large CD30 cells with Hodgkin-like features and their significance have not yet been studied. Here we describe the main clinical, histologic, immunophenotypic, and molecular characteristics of 13 cases of primary cutaneous marginal zone lymphomas featuring >10% of CD30 large cells, and analyze their relationship with histologic and clinical progression of the disease and with other morphologic and immunophenotypic features. We report 10 male and 3 female patients, 4 with early-local disease and 8 with locoregional advanced disease without extracutaneous involvement but with a high relapse rate of 69%. We describe an association between a high level of CD30 expression and disease progression, with increased clinical recurrence in cases with >15% of CD30 cells. We also discuss the differential diagnosis with other cutaneous and systemic lymphomas, especially Hodgkin lymphoma.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.249
Teacher spread0.244 · 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 teacher head, 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

Citations13
Published2019
Admission routes1
Has abstractyes

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