Transitioning to Pegylated Interferon for the Treatment of Cutaneous T‐Cell Lymphoma: Meeting the Challenge of Therapy Discontinuation and a Proposed Algorithm
Bibliographic record
Abstract
Cutaneous T‐cell lymphoma (CTCL) is an uncommon non‐Hodgkin lymphoma characterized by skin involvement, with the most recognized subtypes being mycosis fungoides (MF) and Sezary syndrome (SS). Interferon has been an established treatment for MF/SS since 1984 and is integrated into management guidelines internationally. In 2019, manufacturers abruptly discontinued interferon‐α2b and interferon‐α2a. Many alternative systemic therapies in MF/SS remain unfunded or unavailable in Canada, presenting a unique challenge. Although off‐label use of pegylated interferon is a logical substitute, there are no established dosing guidelines and limited published experience. This case series provides a single‐center experience on pegylated interferon‐α2b for treatment of MF/SS, a suggested management algorithm, and a review of the literature. All patients identified in the Calgary Cutaneous Lymphoma Program with stage IIB–IVB MF/SS treated with interferon‐α2b (4.5–9 MU/week) were switched to once weekly pegylated interferon (90 μg, 0.5 mL) between February and July 2021. Response was monitored using the mSWAT and SkinDex‐29 tools. Eight patients were switched to pegylated interferon, with a median disease duration of 69 months (range: 8–275 months). Five out of eight patients remain on pegylated interferon, with the remainder having switched to preplanned therapies. Two patients required dose reduction due to side effects, including grade II anemia and mood changes. The remaining patients had normal laboratory investigations and no additional side effects. Uncommon lymphomas like MF/SS have limited treatment options, and the impact of abrupt product discontinuation is substantial. We propose a management algorithm for the transition of patients from interferon to pegylated interferon.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| 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".