Ovarian Cancer in ManitobA: Trends in Incidence and Survival, 1992–2011
Bibliographic record
Abstract
Background: Because the International Cancer Benchmarking Partnership, in a study of diagnosis years between 1995 and 2007, showed lower-than-expected survival for Manitoba’s ovarian cancer patients, we undertook an analysis to describe the features of ovarian cancer diagnosed in Manitoba during a 20-year period. We also determined the most recent trends in survival to see if the previous results were sustained. Methods: In this retrospective cohort study, ovarian cancer cases diagnosed during 1992–2011 were extracted from the Manitoba Cancer Registry. The incidence of ovarian cancer was calculated for the overall group and for age, morphology, residence, treatment, and stage. Trends over time, with a particular focus on changes that might correlate with poor survival, were analyzed. The 1- and 3-year relative survival rates were also calculated. Results: The incidence of ovarian cancer did not vary over time (p = 0.640), even when stratified by age or morphology groups. Use of adjuvant chemotherapy decreased (p = 0.005) and use of neoadjuvant chemotherapy increased over time (p = 0.002). Diagnoses of stage iv cancers declined over time (p < 0.020). Trends in incidence did not coincide with previously observed decreases in relative survival. Conclusions: A decline in diagnoses of stage iv ovarian cancer could be responsible for a recent increase in relative survival. However, sample size might have limited power in some analyses, and the previously reported decrease in relative survival might have been due to a random fluctuation in the data. Future efforts will focus on continued monitoring of the patterns of ovarian cancer presentation and outcomes in Manitoba.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".