Cervical cancer: the increasing incidence of adenocarcinoma and adenosquamous carcinoma in younger women.
Bibliographic record
Abstract
Incidence rates of cervical cancer have declined dramatically in Canada over the last 3 decades, from 19.4 per 100 000 women in 1971 to 8.4 per 100 000 women as estimated in 2000.1 The effectiveness of Papanicolaou smear screening for cervical cancer in reducing morbidity and mortality has been well documented in many countries, including Canada.2,3,4,5,6 Up to 90% of cervical cancers are of the squamous cell type, whereas the majority of the remainder are adenocarcinoma and the less common adenosquamous carcinoma. Recent studies from Sweden,5 the United States7 and Australia8 have reported that the incidence of invasive cervical adenocarcinoma, which used to account for 10%–15% of all cervical cancers, has been steadily increasing in young women,9even as the overall incidence of cervical cancer has declined. The cause of the increase is unclear, but it is of concern because studies have shown a poorer prognosis for patients with cervical adenocarcinoma than for those with squamous cell carcinoma.9,10 At diagnosis, adenocarcinomas tend to be larger and exhibit a propensity for early lymphatic and hematogenous metastasis.9,11 Few reports are available on the incidence of adenosquamous carcinoma, although a recent study indicated that it was stable.4 No analysis in Canada of incidence trends of cervical cancer by histological subtype has been reported.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".