Association between physical and cognitive function in post menopause: a cross-sectional study
Bibliographic record
Abstract
Functional tests have been performed to predict cognitive decline in postmenopausal women and may be an important tool to identify early reductions in cognitive performance in this population. However, it is still unclear which functional test is more sensitive for detecting cognitive decline in the investigated sample. The aim of this study was to verify the association between functional performance and cognitive function in postmenopausal women and to analyze whether the gait speed of 400 meters (400wt) and Timed Up and Go (TUG) tests are predictors of cognitive function in this sample. One hundred and twenty eight postmenopausal women (60.8 ± 7.9 years) participated in this cross-sectional study. Body composition was assessed using Dual Energy X-ray Absorptiometry (DXA), functional performance by the TUG and 400wt tests, cognitive performance by the Montreal Cognitive Assessment (MoCA) test and muscle strength by maximum voluntary isometric contraction (MVIC) in lower limbs. An association between functional performance and cognitive function was observed in middle-aged postmenopausal women. The TUG test was associated with the MoCA test (B = -0.79; SE = 0.29; p = 0.008). However, no association was observed between 400wt with the MoCA test (B = 3.03; SE = 1.92; p = 0.117). These results show that the TUG test is a good predictor of cognitive decline in postmenopausal middle-aged women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".