Clinical and Histologic Variants of CD8+ Cutaneous T-Cell Lymphomas
Bibliographic record
Abstract
Although the vast majority of CTCL subtypes are of the CD4+ T-helper cell differentiation phenotype, there is a spectrum of CD8+ variants that manifest wide-ranging clinical, histologic, and phenotypic features that inform the classification of the disease. CD8, like CD4, and cytotoxic molecules (including TIA and granzyme) are readily detectable via IHC staining of tissue and, when expressed on the phenotypically abnormal T-cell population, can help distinguish specific CTCL subtypes. Nonetheless, given that the histopathologic differential for CD8+ lymphoproliferative disorders and lymphomas may range from very indolent lymphomatoid papulosis (LyP) to aggressive entities like CD8+ aggressive epidermotropic cytotoxic T-cell lymphoma (AECTCL), CD8 and/or cytotoxic molecule expression alone is insufficient for diagnosis and is not in itself an indicator of prognosis. We present a review of CTCL subtypes that can demonstrate CD8 positivity: CD8+ mycosis fungoides (MF), LyP type D, subcutaneous panniculitis-like T-cell lymphoma (SPTCL), primary cutaneous gamma/delta T-cell lymphoma (PCGDTL), CD8+ AECTCL, and acral CD8+ T-cell lymphoproliferative disorder (acral CD8+ TCLPD). These diseases may have different clinical manifestations and distinctive treatment algorithms. Due to the rare nature of these diseases, it is imperative to integrate clinical, histologic, and immunohistochemical findings to determine an accurate diagnosis and an appropriate treatment plan.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".