Bibliographic record
Abstract
To the Editor:Mycosis fungoides (MF), the most common cutaneous T-cell lymphoma, can mimic clinically and histopathologically diverse common and rare inflammatory skin conditions in its early stage. 1 We report a case in which dupilumab therapy for presumed atopic dermatitis (AD) unmasked underlying MF.A 72-year-old man presented with a 20-year history of pruritic skin eruptions.Examination showed infiltrative facial erythema (Fig. 1A).A biopsy in 2018 was consistent with eczematous dermatitis (Fig. 1B).In June 2023, he was diagnosed with AD and started on dupilumab (300 mg biweekly).While most symptoms improved, his facial lesions worsened paradoxically despite topical triamcinolone acetonide-econazole and tacrolimus ointment.A repeat facial biopsy revealed a dense dermal lymphocytic infiltrate (Fig. 1C).Immunohistochemistry showed CD4 + predominance (Fig. 1D), which, in the clinical context, raised suspicion for MF.Subsequent interferon a-2a therapy achieved significant facial lesion resolution.(Fig. 1E).This case highlights a critical diagnostic challenge.Dupilumab is highly effective for moderate-to-severe AD, 2 but its use has been associated with "dupilumab facial redness"
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".