Patterns in the incidence of age‐related ovarian cancer in South East England 1967–1996
Bibliographic record
Abstract
OBJECTIVE: To study the age-related trends in the incidence rates of ovarian cancer in South East England between 1967 and 1996. DESIGN: A retrospective review of systematically collected data on ovarian cancer in South East England. METHODS: Data were obtained from the Thames Cancer Registry on the numbers and rates per 100,000 population of ovarian cancer per five-year age group (0-85+) in the 30-year period from 1967 to 1996 from the 26 health authorities in the Thames region. Linear regression was performed to determine the changes in incidence rates of ovarian cancer per age group over time. MAIN OUTCOME MEASURES: The change in overall incidence of ovarian cancer in South East England, as well as the change in incidence of ovarian cancer in each five-year age groups (20-85+) in the 30-year study period. RESULTS: There was a strong positive correlation between ovarian cancer rates and year of diagnosis in women aged > or = 70 years, and this was particularly marked in women > 85 years of age. There was a negative correlation between rates and year of diagnosis in women aged 45-59 years. The analysis did not demonstrate a significant correlation between ovarian cancer rates and year of diagnosis in women < 44 years of age or women aged 60-69 years. CONCLUSIONS: There have been significant changes in the pattern of ovarian cancer incidence in South East England during the 30-year period studied. The observed changes in ovarian cancer incidence in younger women may, in part, be explained by known reproductive factors. The rise in ovarian cancer rates among the older age group is difficult to explain, but has important implications for the future planning and provision of cancer services.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".