Incidence, survival and mortality among women with epithelial ovarian cancer by histotype : a population study in British Columbia, Canada
Bibliographic record
Abstract
Objectives. Epithelial Ovarian cancer (EOC) is composed of five distinct histologic subtypes. However, histotype- specific survival and mortality estimates among women with EOC are limited. Also, we lack information on long-term health conditions faced by ovarian cancer survivors. This thesis examined: 1) histotype- specific incidence and survival rates among women with EOC in British Columbia (BC); 2) causes of death among women with EOC by histotype; and, 3) compared these causes of death to age-standardized causes of death in the general population. Methods. Using population-based administrative datasets, I built two population-based cohorts of all women with EOC diagnosed in BC: 1. women diagnosed between 1980 and 2015(cohort 1) and women diagnosed between 1990 and 2014 (cohort 2). Cohort 1 was used to answer question 1, whereas cohort 2 was used to answer question 2 and 3. For question 2, I compared the causes of death within histotypes, by age at diagnosis, BRCA status, and time since diagnosis. For question 3, I calculated all-cause and cause-specific standardized mortality ratios. Results. Decreasing incidence rates and increasing survival rates were observed among women with EOC in BC. As expected, ovarian cancer was the most common cause of death among these women, which was first surpassed by other causes of deaths at 11 years after diagnosis. When stratified by serous and non- serous EOCs, ovarian cancer was the leading cause of death for 12 years and for 8 years respectively after diagnosis. Of particular interest, the number of deaths from other cancers (breast, colorectal and lung cancer) and external causes (falls) among long-term ovarian cancer survivors (5-9 and 10+ years post diagnosis) were higher than the expected. Conclusions. Although there was an improvement in the survival over time, ovarian cancer remains the leading cause of death for 11 years following diagnosis of EOC. My findings suggest that long-term survivors (those living 5-9 and 10+ years following diagnosis) are particularly vulnerable to deaths from other cancers and from falls in elderly survivors. Hence, these women may benefit from closer surveillance of other cancers and bone health.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".