Diet and survival after a diagnosis of ovarian cancer: a pooled analysis from the Ovarian Cancer Association Consortium
Bibliographic record
Abstract
BACKGROUND: Prognosis after a diagnosis of invasive epithelial ovarian cancer is poor. Some studies have suggested modifiable behaviors, like diet, are associated with survival but the evidence is inconsistent. OBJECTIVES: This study aims to pool data from studies conducted around the world to evaluate the relationships among dietary indices, foods, and nutrients from food sources and survival after a diagnosis of ovarian cancer. METHODS: This analysis from the Multidisciplinary Ovarian Cancer Outcomes Group within the Ovarian Cancer Association Consortium included 13 studies with 7700 individuals with ovarian cancer, who completed food-frequency questionnaires regarding their prediagnosis diet. Adjusted hazard ratios (aHRs) and 95% confidence intervals (CI) for associations with overall survival were estimated using Cox proportional hazards models. RESULTS: Overall, there was no association between any of the 7 dietary indices (representing prediagnosis diet) evaluated and survival; however, associations differed by tumor stage. Although there were no consistent associations among those with advanced disease, among those with earlier stage (local/regional) disease, higher scores on the alternate Healthy Eating Index (aHR quartile 4 compared with 1 = 0.66, 95% CI: 0.50, 0.87), Healthy Eating Index-2015 (aHR: 0.75; 95% CI: 0.59, 0.97), and alternate Mediterranean diet (aHR: 0.76; 95% CI: 0.60, 0.98) were associated with better survival. Better survival was also observed for individuals with early-stage disease who reported higher intakes of dietary components that contribute to the healthy diet indices (aHR for Q4 compared with Q1: vegetables 0.71; 95% CI: 0.56, 0.91), tomatoes (aHR: 0.72; 95% CI: 0.57, 0.91) and nuts and seeds (aHR 0.71; 95% CI: 0.55, 0.92). In contrast, there were suggestions of worse survival with higher scores on 2 of the 3 inflammatory indices and higher intake of trans-fatty acids. CONCLUSIONS: Adherence to a more healthy, less-inflammatory diet may confer a survival benefit for individuals with early-stage ovarian cancer.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.021 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".