No differences in performance on test of working memory and executive functioning between healthy elderly postmenopausal women using or not using hormone therapy
Bibliographic record
Abstract
BACKGROUND: On average, ovarian function ceases at the age of 52 years so that estrogen (E) levels are chronically low following the menopause. Numerous studies have found that hormone therapy (HT) helps to protect verbal memory, a hippocampal function. Estrogen receptors are also found in the prefrontal cortex (PFC), suggesting that estrogen may modulate executive and working memory functions, both mediated by the PFC. The possible role of progesterone (P) on executive functions and working memory is unknown. OBJECTIVE: To examine the relationship between neuropsychological performance, age of initiation of HT, and duration of HT use. METHOD: In this cross-sectional study, the neuropsychological performance of 37 postmenopausal women (mean age, 65 years) who used either estrogen-only or sequential E + P (E-alone group)(n = 22) or E + P continuously (n = 15) was compared to that of 28 healthy postmenopausal women matched for age and education who had never used HT. It was hypothesized that the E-only users would perform better then the E + P and the never-users on neuropsychological tests of verbal memory, executive function and working memory. RESULTS: Results showed only minor between-group differences on working memory scores such that the E + P users were slowest to generate a response on the N-Back test of working memory. No group differences on tests of executive functions were found. CONCLUSION: There was no relationship between neuropsychological performance, age of initiation of HT, or duration of HT use.
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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) | 0.002 | 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".