Abstract 5906: Epidemiologic risk factors and survival trajectories among epithelial ovarian cancer survivors: A population-based cohort study
Bibliographic record
Abstract
Abstract Background: Despite a poor 5-year survival rate of 45%, survival following a diagnosis of epithelial ovarian cancer stabilizes after 10 years. Although various clinical and tumor characteristics are established prognostic factors, the role of epidemiologic risk factors on short- versus long-term survival are unclear. The aim of this study was to evaluate the association between various hormonal, reproductive, and lifestyle risk factors on ovarian cancer mortality by survival trajectory time intervals across the survivorship phase. Methods: This population-based retrospective cohort study included 1,421 unselected women diagnosed with epithelial ovarian cancer from 1995 to 2004 in Ontario, Canada. Clinical information was obtained from medical records, risk factor information from telephone interview, and vital status was updated by linkage to the Ontario Cancer Registry up until 2020. We examined clinical risk factors, hormonal and reproductive related factors, lifestyle factors, and family history. Extended Cox proportional hazards models with Heaviside functions were used to estimate the association between risk factors and mortality by survival trajectory time interval (<3, 3-<6, 6-<10, and ≥10 years). Results: After a mean follow-up of 11.6 years, 65% (n=926) subjects died of which 51% (n=731) were due to ovarian cancer. Clinical factors such as late stage and presence of residual disease were strongly associated with short-term mortality, which attenuated over survival time. Stage IV disease significantly increased the risk of mortality compared to stage I disease for the survival interval within 3 years of diagnosis (HR 50.05; 95% CI 6.78, 369.57), however this association declined for survival beyond 10 years (HR 2.76; 95% CI 1.35, 5.65). Similarly, presence of residual disease increased the risk of mortality in the short-term (HR 2.47; 95% CI 1.40, 4.34), yet attenuated for long-term survival (HR 1.26; 95% CI 0.83, 1.93). Risk factors such as breastfeeding, smoking, and BMI were not associated with short-term survival, but significantly associated with long-term survival. History of breastfeeding decreased the risk of mortality (HR 0.65; 95% CI 0.46, 0.93), while a history of smoking (HR 1.75; 95% CI 1.27, 2.40) and obesity (HR 1.81; 95% CI 1.24, 2.65) increased the risk of mortality among long-term survivors. Conclusions: This study confirmed that clinical risk factors such as stage at ovarian cancer diagnosis and residual disease following debulking surgery are important prognostic factors in the short-term survival trajectory while breastfeeding, smoking, and BMI play a stronger role for long-term ovarian cancer survival. These findings suggest a role of modifiable factors in improving long-term outcomes across the survivorship phase. Citation Format: Shana Jean Kim, Barry Rosen, John R. McLaughlin, Harvey Risch, Shelly S. Tworoger, Steven A. Narod, Joanne Kotsopoulos. Epidemiologic risk factors and survival trajectories among epithelial ovarian cancer survivors: A population-based cohort study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5906.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".