Vitamin D Exposure and Ovarian Cancer Risk and Prognosis
Bibliographic record
Abstract
Given the poor prognosis of ovarian cancer and limited population-level strategies for early detection and long-term treatment success, knowledge of modifiable risk factors for prevention and improved prognosis is important. Vitamin D has received wide scientific interest in cancer research as having the potential to be one such factor. We carried out a systematic narrative review of the literature on vitamin D and ovarian cancer risk and survival. We included 17 case-control and cohort studies on ovarian cancer incidence. Five analyses were of sun exposure, among which three reported an inverse association. Of 11 analyses of dietary vitamin D, two reported an inverse association. Among five studies of 25(OH)D levels, an inverse association was reported in two. Across all studies the findings were inconsistent, but some recent studies have suggested that vitamin D exposure at earlier ages may be important. Only three studies examining vitamin D exposure in relation to survival among ovarian cancer survivors were identified and the findings were inconsistent. The evidence to date supports a null influence of vitamin D on both ovarian cancer risk and survival. Future research should ensure that exposure assessment captures vitamin D exposure from all sources and for the etiologically or prognostically pertinent period.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".