Associated Hematolymphoid Malignancies in Patients With Lymphomatoid Papulosis: A Canadian Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Lymphomatoid papulosis is one of the primary cutaneous CD30+ T-cell lymphoproliferative disorders. Although considered a benign disease, lymphomatoid papulosis has been associated potentially with an increased risk of secondary hematolymphoid malignancies. OBJECTIVE: The aim of this study was to assess the clinical characteristics and histologic subtypes of lymphomatoid papulosis, identify the prevalence and types of secondary hematolymphoid malignancies, and determine the potential risk factors for development of these hematolymphoid malignancies. METHODS AND MATERIALS: A retrospective chart review was performed for all histologically confirmed cases of lymphomatoid papulosis between 1991 and 2016. RESULTS: Seventy patients with lymphomatoid papulosis were identified. Thirty patients (43%) experienced a secondary hematolymphoid malignancy. Twenty-four (80%) of the hematolymphoid malignancies occurred after the onset of lymphomatoid papulosis. Older age at diagnosis of lymphomatoid papulosis, male sex, histology type B, and the presence of T-cell receptor gene rearrangement are associated with higher risk of developing hematolymphoid malignancy. CONCLUSION: Lymphomatoid papulosis is associated with increased risk of developing secondary hematolymphoid malignancies, particularly mycosis fungoides and cutaneous anaplastic large cell lymphoma.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".