Vulvar cancer in high‐income countries: Increasing burden of disease
Bibliographic record
Abstract
The aim of this study was to assess trends in the age-specific incidence of vulvar cancer in 13 high-income countries satisfying a priori conditions regarding the availability of cancer registry data over a 20-year period; these were Canada, the United States, nine European countries, Australia and Japan. Five-yearly incidence and population at risk were obtained from the International Agency for Research on Cancer's Cancer Incidence in Five Continents for the years 1988-1992 (Volume 7) to 2003-2007 (Volume 10). The 5-yearly average percent change (AvPC) over the period and standardised rate ratios (SRRs) for 2003-2007 versus 1988-1992 were used to assess changes in the age-standardised incidence rates of vulvar cancer for all ages, and for <60 years and 60+ years. During the study period, the 5-yearly AvPC across the 13 countries increased by 4.6% (p = 0.005) in women of all ages, and 11.6% (p = 0.02) in those <60 years. No change was observed in women aged 60+ years (5-yearly AvPC = 0.1%, p = 0.94). The SRR for 2003-2007 versus 1988-1992 was significantly elevated in women <60 years of age (SRR = 1.38, 95% CI: 1.30-1.46), but not in women of 60+ years (SRR = 1.01, 95% CI: 0.97-1.05). The increase in incidence in women <60 years of age drove a significant increase in the overall SRR in women of all ages (SRR = 1.14, 95% CI: 1.11-1.18). Some differences in the specific findings at the individual country level were observed. The findings are consistent with changing sexual behaviours and increasing levels of exposure to human papillomavirus (HPV) in cohorts born around/after about 1950, but younger cohorts offered HPV vaccination are likely to receive some protection against developing vulvar cancer in the future.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".