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Record W4224318531 · doi:10.3390/curroncol29050246

Cutaneous Involvement in Diseases with Plasma Cell Differentiation: Diagnostic Approach

2022· article· en· W4224318531 on OpenAlexvenueno aff
Magda Zanelli, Andrea Palicelli, Francesca Sanguedolce, Maurizio Zizzo, Alessandra Filosa, Linda Ricci, Camilla Cresta, Giovanni Martino, Alessandra Bisagni, Eleonora Zanetti, Francesco di Donato, Beatrice Melli, Alessandra Soriano, Luca Cimino, Alessia Cavazza, Lisa Francesca Vivian, Stefano Ascani

Bibliographic record

VenueCurrent Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePlasma cellComputational biologyBioinformaticsPathologyImmunologyBiologyMultiple myeloma

Abstract

fetched live from OpenAlex

Neoplasms with plasma cell differentiation may occasionally involve the skin. Cutaneous lesions may represent the first sign of an underlying systemic plasma cell malignancy, such as multiple myeloma, or the skin itself may be the primary site of occurrence of a hematological tumor with plasma cell differentiation. Starting from examples encountered in our daily practice, we discussed the diagnostic approach pathologists and clinicians should use when faced with cutaneous lesions with plasma cell differentiation. Cases of primary cutaneous marginal zone lymphoma, localized primary amyloidosis/amyloidoma, and cutaneous manifestations (secondary either to multiple myeloma or to plasmablastic lymphoma) are discussed, focusing on the importance of the adequate patient's work-up and precise clinicopathological correlation to get to the correct diagnosis and appropriate treatment. The pertinent literature has been reviewed, and the clinical presentation, pathological findings, main differential diagnoses, treatment, and outcome of neoplasms with plasma cell differentiation involving the skin are discussed.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

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

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 head0.057
GPT teacher head0.341
Teacher spread0.284 · 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 designObservational
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

Citations8
Published2022
Admission routes1
Has abstractyes

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