Long-term mortality among women with epithelial ovarian cancer: a population-based study in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVES: Among women with epithelial ovarian cancer (EOC), histotype is one of the major prognostic factors. However, few data are available on histotype- specific survival and mortality estimates among these patients. We therefore examined survival and causes of death among women with EOC by histotype. METHODS: A population- based cohort including all ovarian cancer patients diagnosed in British Columbia (BC) between 1990 and 2014 was built using population-based administrative datasets. We compared causes of death within histotypes, by age at diagnosis, BRCA status, and time since diagnosis. RESULTS: A total of 6975 women were identified as having been diagnosed with EOC between 1990 and 2014 in BC. The most common cause of death among these women was ovarian cancer until 10 years post diagnosis when other causes surpassed ovarian cancer as the leading cause of death. Among women with serous EOCs, ovarian cancer was the leading cause of death 12 years after diagnosis, whereas ovarian cancer was the leading cause of death for 8 years among women with non- serous EOCs. Among women with serous EOCs, ovarian cancer was the leading cause of death for 12 years among younger women (< 60 years of age) compared to 8 years among women > = 60 years of age, and those with BRCA mutations were more likely to die from ovarian cancer than those without a BRCA mutation. CONCLUSIONS: Within 10 years from diagnosis, ovarian cancer is the leading cause of death among women diagnosed with EOC.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".