RANKL and OPG and their influence on breast volume changes during pregnancy in healthy women
Bibliographic record
Abstract
Objectives: Breast cancer risk is influenced both by number of pregnancies and breastfeeding duration, but not all women benefit similarly from these. Breast volume changes during pregnancy may be predictive for later transformation of the breast. This study investigated the influence of serum RANKL and OPG at the start of pregnancy on breast volume changes. Methods: In the Clinical Gravidity Association Trial and Evaluation (CGATE) program, pregnant women were followed prospectively from gestational week 12 to birth. Three-dimensional breast surface imaging and volume assessments were performed. Serum RANKL and OPG were measured at study entry before gestational week 12. A linear regression model including breast volume at the start of pregnancy, RANKL, OPG, and other factors was used to predict breast volume at term. Results: The mean breast volume was 413 mL at the start of pregnancy, increasing by a mean of 99 mL up to term. In addition to body mass index and breast volume at the start of pregnancy, RANKL and OPG also appeared to influence volume ( P = 0.04 and P = 0.07, respectively). Women with measurable RANKL values had a mean volume increase 32 mL larger than in women without measurable RANKL. The presence of OPG reduced the mean volume change by 27 mL. Conclusions: This is the first study showing that RANKL and OPG influence breast volume changes during pregnancy. Understanding the molecular mechanisms behind the effects of pregnancy on the breast might help in developing strategies for mimicking pregnancy effects in order to reduce breast cancer risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".