Trends in the Incidence of Invasive and In Situ Vulvar Carcinoma
Bibliographic record
Abstract
OBJECTIVE: To characterize the incidence of vulvar carcinoma in situ and vulvar cancer over time. METHODS: We used the Surveillance Epidemiology and End Results database to assess trends in the incidence of vulvar cancer over a 28-year period (1973 through 2000) and determined whether there had been a change in incidence over time. Information collected included patient characteristics, primary tumor site, tumor grade, and follow-up for vital status. We calculated the incidence rates by decade of age, used chi(2) tests to compare demographic characteristics, and tested for trends in incidence over time. RESULTS: A total of 13,176 in situ and invasive vulvar carcinomas were identified; 57% of the women were diagnosed with in situ, 44% with invasive disease. Vulvar carcinoma in situ increased 411% from 1973 to 2000. Invasive vulvar cancer increased 20% during the same period. The incidence rates for in situ and invasive vulvar carcinomas are distributed differently across the age groups. In situ carcinoma incidence increases until the age of 40-49 years and then decreases, whereas invasive vulvar cancer risk increases as a woman ages, increasing more quickly after 50 years of age. CONCLUSION: The incidence of in situ vulvar carcinoma is increasing. The incidence of invasive vulvar cancer is also increasing but at a much lower rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".