Breast cancer: trends in international incidence in men and women
Bibliographic record
Abstract
BACKGROUND: The age-standardised incidence of breast cancer varies geographically, with rates in the highest-risk countries more than five times those in the lowest-risk countries. METHODS: We investigated the correlation between male (MBC) and female breast cancer (FBC) incidence stratified by female age-group (<50 years, and ≥50 years) and used Poisson regression to examine male incidence rate ratios according to female incidence rates. RESULTS: Age-adjusted breast cancer incidence rates for males and females share a similar geographic distribution (Spearman's correlation=0.51; P<0.0001). A correlation with male incidence rates was found for the entire female population and for women aged 50 years and over. Breast cancer incidence rates in males aged <50 years were not associated with FBC incidence, whereas those in males aged 50 years were. MBC incidence displays a small 'hook' similar to the Clemmesen's hook for FBC, but at a later age than the female hook. INTERPRETATION: Further investigation of possible explanations for these patterns is warranted. Although the incidence of breast cancer is much lower in men than in women, it may be possible to identify a cause common to both men and 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".