A Tale of Two Time Periods: Ovarian Cancer Trends in Ontario
Bibliographic record
Abstract
We assessed population-based trends in incidence and survival rates for epithelial ovarian cancer in Ontario in two time periods. Our population-based study cohort included all women with epithelial ovarian cancer treated initially with abdominal surgery in Ontario for January 1996 through December 2001. Incident surgical cases were documented by hospital contact data and the Ontario Cancer Registry. Patient characteristics (age, for example) were obtained from electronic administrative data records. Regression analyses were used to assess the influence of time period on survival while controlling for age, comorbidity, and other factors associated with this outcome.A total of 3825 women met the inclusion criteria. We found that the age-standardized incidence of ovarian cancer remained stable during 1996-2001. A shift to a younger age at diagnosis was found between the two time periods being compared. The univariate analysis revealed a clear difference in death rate, to which age at diagnosis, Charlson comorbidity score, and treatment period contributed. Earlier time period (p < 0.0001), advancing age (p < 0.0001), higher Charlson score (p < 0.0001), and lower income quartile score (p = 0.03) were significantly associated with poorer survival in the univariate analysis. Younger age, lower Charlson score, and more recent time period of diagnosis and treatment (p < 0.0001) were associated with improved survival in the proportional hazards model.We conclude that age-standardized incidence and mortality rates for ovarian cancer in Ontario have remained stable. For women initially treated with surgery, advances in management have led to an improvement in survival.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".