Anthropometric factors and risk of melanoma in women: A pooled analysis
Bibliographic record
Abstract
Anthropometric factors such as height, weight and body mass index are related to the occurrence of certain malignancies in women including cancers of the breast, ovary and endometrium. Several studies have investigated the relation between height and weight or body mass and the risk of cutaneous melanoma in women, but results have been inconsistent. We conducted a collaborative analysis of these factors using the original data from 8 case-control studies of melanoma in women (2,083 cases and 2,782 controls), with assessment of the potential confounding effects of socioeconomic, pigmentary and sun exposure-related factors. Women in the highest quartile of height had an increased risk of melanoma [pooled odds ratio (pOR) 1.3, 95% confidence interval (CI) 1.1-1.6]. We also found an elevated risk associated with weight gain in adult life of 2 kg or more (pOR 1.5, 95% CI 1.1-2.0). Stratifying by age at melanoma diagnosis (<50, >or=50 yr), we found this risk greater among women <50 yr of age. Associations were unaffected by adjustment for other known risk factors for melanoma. There was no evidence that the effects varied for different histologic subtypes of cutaneous melanoma. There was no association with body weight per se, body mass index, or body surface area, either recent or in young adulthood. In aggregate, data from these studies suggest that greater height and weight gain may be risk factors for cutaneous melanoma in women.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.020 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".