Predicted 25-hydroxyvitamin D over the adult lifetime and the risk of ovarian cancer
Bibliographic record
Abstract
The evidence from previous studies of serum 25-hydroxyvitamin D (25(OH)D) and ovarian cancer risk is not conclusive. However, the 25(OH)D levels were generally only measured in late adulthood, which may not capture the etiologically relevant exposure periods. We investigated predicted 25(OH)D over the adult lifetime in relation to ovarian cancer risk in a population-based case-control study conducted from 2011 to 2016 in Montreal, Canada (n = 490 cases and 896 controls). Predicted 25(OH)D was computed using previously validated regression models. Unconditional multivariable logistic regression models were used to estimate adjusted odds ratios (aORs) and 95% CIs for average predicted 25(OH)D over the adult lifetime and ovarian cancer risk. In addition, the relative importance of different periods of past 25(OH)D exposure was explored using a weighted cumulative exposure (WCE) model. For each 20-nmol/L increase in average predicted 25(OH)D over the adult lifetime, the aOR (95% CI) was 0.73 (0.55-0.96). In WCE analyses, the inverse association was strongest for exposures 5 to 20 years and 35 to 55 years prior to diagnosis, with aORs (95% CIs) of 0.82 (0.69-0.94) and 0.79 (0.66-1.02), respectively, for each 20-nmol/L increase in predicted 25(OH)D. These results support an inverse association between 25(OH)D levels in adulthood and ovarian cancer risk. This article is part of a Special Collection on Gynecological Cancers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".