© 1999 Cancer Research Campaign Article no. bjoc.1999.0665 Ovarian cancer histology-specific incidence trends in
Bibliographic record
Abstract
Summary This study examined histology-specific incidence trends of ovarian cancer in Canada, 1969–1993. The impact of age, period and cohort effects on these trends were studied by means of age-period-cohort analysis. Age-standardized incidence rates of serous, endometrioid, clear cell and germ cell tumours increased significantly and the rates of sex cord-stromal and other classified epithelial ovarian tumours decreased considerably. The rates of mucinous and NOS/unclassified tumours remained unchanged. Cohort effect has a major impact on incidence trends of serous, endometrioid, germ cell, sex cord-stromal and other classified epithelial ovarian tumours but no meaningful impact on trends of mucinous, clear cell, or NOS/unclassified ovarian tumours. Various cohort patterns by histology subtypes were observed: the risk of developing serious tumours increased markedly among birth cohorts of 1895–1930, stabilized thereafter and decreased among young cohorts of 1950–1960; the risk of germ cell tumours increased significantly among young cohorts of 1965–1980; and the risk of sex cord-stromal tumours dropped constantly among cohorts 1910–1950. Various period patterns by histology subtypes observed in this study suggested changes in histology classification criteria over the period. Further studies need to consider the various etiologies and the classification criteria changes according to histology subtypes.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".