Medical History, Lifestyle, Family History, and Occupational Risk Factors for Mycosis Fungoides and Sezary Syndrome: The InterLymph Non-Hodgkin Lymphoma Subtypes Project
Bibliographic record
Abstract
BACKGROUND: Mycosis fungoides and Sézary syndrome (MF/SS) are rare cutaneous T-cell lymphomas. Their etiology is poorly understood. METHODS: A pooled analysis of 324 MF/SS cases and 17217 controls from 14 case-control studies from Europe, North America, and Australia, as part of the International Lymphoma Epidemiology Consortium (InterLymph) Non-Hodgkin Lymphoma (NHL) Subtypes Project, was carried out to investigate associations with lifestyle, medical history, family history, and occupational risk factors. Multivariate logistic regression models were used to calculate odds ratios (OR) and 95% confidence intervals (CI). RESULTS: We found an increased risk of MF/SS associated with body mass index equal to or larger than 30 kg/m(2) (OR = 1.57, 95% CI = 1.03 to 2.40), cigarette smoking for 40 years or more (OR = 1.55, 95% CI = 1.04 to 2.31), eczema (OR = 2.38, 95% CI = 1.73 to 3.29), family history of multiple myeloma (OR = 8.49, 95% CI = 3.31 to 21.80), and occupation as crop and vegetable farmers (OR = 2.37, 95% CI = 1.14 to 4.92), painters (OR = 3.71, 95% CI = 1.94 to 7.07), woodworkers (OR = 2.20, 95% CI = 1.18 to 4.08), and general carpenters (OR = 4.07, 95% CI = 1.54 to 10.75). We also found a reduced risk of MF/SS associated with moderate leisure time physical activity (OR = 0.46, 95% CI = 0.22 to 0.97). CONCLUSIONS: Our study provided the first detailed analysis of risk factors for MF/SS and further investigation is needed to confirm these findings in prospective data and in other populations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".