Gray matter volume in women with the BRCA mutation with and without ovarian removal
Bibliographic record
Abstract
ABSTRACT Objective Ovarian removal prior to spontaneous/natural menopause (SM) is associated with increased risk of late life dementias including Alzheimer’s disease. This increased risk may be related to the sudden and early loss of endogenous estradiol. Women with breast cancer gene mutations (BRCAm) are counselled to undergo oophorectomy prior to SM to significantly reduce their risk of developing breast, ovarian, and cervical cancers. There is limited evidence of the neurological effects of ovarian removal prior to the age of SM and this is in women without the BRCAm showing cortical thinning in medial temporal lobe structures. A second study in women with BRCAm and BSO noted changes in cognition. Methods The present study looked at whole-brain changes in gray matter volume using high-resolution, quantitative MRI in women with BRCAm and intact ovaries (BRCA-preBSO) and after surgery with (BSO+ERT) and without (BSO) post-surgical estradiol hormone replacement therapy compared with agematched women (AMC) with their ovaries. Results The BRCA-preBSO and BSO groups showed significant gray matter loss in left medial temporal and frontal lobe structures. BSO+ERT exhibited few areas of gray matter reductions compared to AMC. Novel to this study, we also observed that all three BRCAm groups exhibited gray matter increases compared with AMC, suggesting continued plasticity. Conclusions The present study provides evidence, through gray matter volume reductions, to support both the possibility that the BRCAm, alone, and early life BSO may play a role in increasing the risk for late-life dementia. At least for BRCAm with BSO, post-surgical ERT seems to ameliorate gray matter losses.
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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 | 0.000 |
| 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".