Later age at menopause was associated with higher cognitive function in post-menopausal women
Bibliographic record
Abstract
BACKGROUND Menopause is a condition in which the menstrual periods have stopped for the last 12 months due to cessation of ovarial functions causing estrogen hormones to decrease. Various studies find that many factors affect cognitive function at post-menopausal age among others the decrease in estrogens, age at menopause, duration of menopause, and education. However, the effects have been subject to controversy. The aim of this study was to determine the relationship of age, age at menopause, estradiol level, and education with cognitive function among healthy post-menopausal women. METHODS A cross-sectional study was conducted involving 31 post-menopausal women between 50 to 75 years old. Data on age, age at menopause, and education were collected using a questionnaire. The estradiol levels were measured using an electrochemiluminescent immunoassay (ECLIA). The Indonesian version of the Montreal Cognitive Assessment (MoCA INA) was used to assess the cognitive function. Multiple linear regression was used to analyze the data. A p<0.05 was considered statistically significant.RESULTS Age (b=-0.086; 95% C.I.=-0.263-0.090; p=324) and estradiol levels (b=0.106; 95% C.I.=-0.018 -0.230; p=0.092) were not significantly associated with cognitive function. However, education (b=1.537; 95% C.I.=0.176-2.898; p=0.028) and age at menopause (b=0.364;0.056-0.671; p=0.022) were significantly associated with cognitive function. Age at menopause was the most influential factor of cognitive function (Beta=0.402) compared to education (Beta=0.394).CONCLUSION Later age at menopause could increase cognitive function in post-menopausal women. Our findings are that modifiable factors that delay age at menopause should receive attention, in order to promote cognitive function. Keywords: Age at menopause, estrogens, cognitive function, post-menopausal women
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".