Non‐Hodgkin lymphoma and obesity: A pooled analysis from the InterLymph Consortium
Why this work is in the frame
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Bibliographic record
Abstract
Nutritional status is known to alter immune function, a suspected risk factor for non-Hodgkin lymphoma (NHL). To investigate whether long-term over, or under, nutrition is associated with NHL, self-reported anthropometric data on weight and height from over 10,000 cases of NHL and 16,000 controls were pooled across 18 case-control studies identified through the International Lymphoma Epidemiology Consortium. Study-specific odds ratios (OR) were estimated using logistic regression and combined using a random-effects model. Severe obesity, defined as BMI of 40 kg m(-2) or more, was not associated with NHL overall (pooled OR = 1.00, 95% confidence interval (CI) 0.70-1.41) or the majority of NHL subtypes. An excess was however observed for diffuse large B-cell lymphoma (pooled OR = 1.80, 95% CI 1.24-2.62), although not all study-specific ORs were raised. Among the overweight (BMI 25-29.9 kg m(-2)) and obese (BMI 30-39.9 kg m(-2)), associations were elevated in some studies and decreased in others, while no association was observed among the underweight (BMI < 18.5 kg m(-2)). There was little suggestion of increasing ORs for NHL or its subtypes with every 5 kg m(-2) rise in BMI above 18.5 kg m(-2). BMI components height and weight were also examined, and the tallest men, but not women, were at marginally increased risk (pooled OR = 1.19, 95% CI 1.06-1.34). In summary, whilst we conclude that there is no evidence to support the hypothesis that obesity is a determinant of all types of NHL combined, the association between severe obesity and diffuse large B-cell lymphoma may warrant further investigation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it