Cutaneous Involvement in Diseases with Plasma Cell Differentiation: Diagnostic Approach
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".