When mycosis fungoides seems not to be within the spectrum of clinical and histopathological differential diagnoses
Bibliographic record
Abstract
The most prevalent primary cutaneous T-cell lymphoma, mycosis fungoides (MF), is characterized by the development of plaques and nodules after an erythematous patchy phase that is non-specific. An infiltrate of atypical small- to medium-sized cerebriform lymphocytes in the superficial dermis, with variable epidermotropism, is the histopathological hallmark of the disease. In more advanced stages of the illness, large-cell transformation may be seen. Early diagnosis of MF can be very challenging based only on histopathologic or clinical findings, so it is critical to have a clinical-pathological correlation. Many atypical variants of MF that deviate from the classic Alibert-Bazin presentation of the disease have been described over the past 30 years, sometimes with different prognostic and therapeutic implications. Clinically or histopathologically, they can mimic a wide range of benign inflammatory skin disorders. To make a conclusive diagnosis in these cases, it is recommended to take multiple biopsies from various lesions and to carefully correlate the clinical and pathological findings. We have outlined the various facets of the illness in this review, positioning MF as a "great imitator", with an emphasis on the more recently identified variations, differential diagnosis, and its benign mimics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".