Medical History, Lifestyle, Family History, and Occupational Risk Factors for Peripheral T-Cell Lymphomas: The InterLymph Non-Hodgkin Lymphoma Subtypes Project
Bibliographic record
Abstract
BACKGROUND: Accounting for 10%-15% of all non-Hodgkin lymphomas in Western populations, peripheral T-cell lymphomas (PTCL) are the most common T-cell lymphoma but little is known about their etiology. Our aim was to identify etiologic risk factors for PTCL overall, and for specific PTCL subtypes, by analyzing data from 15 epidemiologic studies participating in the InterLymph Consortium. METHODS: A pooled analysis of individual-level data for 584 histologically confirmed PTCL cases and 15912 controls from 15 case-control studies conducted in Europe, North America, and Australia was undertaken. Data collected from questionnaires were harmonized to permit evaluation of a broad range of potential risk factors. Odds ratios (OR) and 95% confidence intervals (CI) were calculated using logistic regression. RESULTS: Risk factors associated with increased overall PTCL risk with a P value less than .05 included: a family history of hematologic malignancies (OR = 1.92, 95% CI = 1.30 to 2.84); celiac disease (OR = 17.8, 95% CI = 8.61 to 36.79); eczema (OR = 1.41, 95% CI = 1.07 to 1.85); psoriasis (OR = 1.97, 95% CI = 1.17 to 3.32); smoking 40 or more years (OR = 1.92, 95% CI = 1.41 to 2.62); and employment as a textile worker (ever) (OR = 1.58, 95% CI = 1.05 to 2.38) and electrical fitter (ever) (OR = 2.89, 95% CI = 1.41 to 5.95). Exposures associated with reduced overall PTCL risk included a personal history of allergies (OR = 0.69, 95% CI = 0.54 to 0.87), alcohol consumption (ever) (OR = 0.64, 95% CI = 0.49 to 0.82), and having ever lived or worked on a farm (OR = 0.72, 95% CI = 0.55% to 0.95%). We also observed the well-established risk elevation for enteropathy-type PTCL among those with celiac disease in our data. Conclusions Our pooled analyses identified a number of new potential risk factors for PTCL and require further validation in independent series.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".