Arthralgia in midlife Singaporean women: the Integrated Women’s Health Program (IWHP)
Bibliographic record
Abstract
OBJECTIVE: Arthralgia is a common menopausal complaint in midlife women, and its causes remain unclear. We examined the prevalence of menopausal arthralgia with various factors including sleep quality, depression/anxiety, muscle strength and physical performance among midlife Singaporean women. METHODS: The Integrated Women's Health Program (IWHP) comprised 1120 healthy, community-dwelling women of Chinese, Malay or Indian ethnicities (aged 45-69 years) attending well-women clinics at the National University Hospital, Singapore. Sociodemographic, menopausal, reproductive and health data were obtained with validated questionnaires. Muscle strength, physical performance and dual-energy X-ray absorptiometry were measured. Women with moderate to very severe symptoms using the Menopause Rating Scale were classified as having arthralgia. Multivariable logistic regression analyses examined risk factors for arthralgia. RESULTS: One-third of the participants reported arthralgia, and 12.7%, 16.2% and 71.2% were in the premenopausal, perimenopausal and postmenopausal period, respectively. Menopausal symptoms, such as vaginal dryness (adjusted odds ratio [aOR]: 2.64, 95% confidence interval [CI]: 1.64, 4.24) and physical/mental exhaustion (aOR: 2.83, 95% CI: 1.79, 4.47), were independent risk factors for arthralgia. Poor muscle strength (aOR: 2.20, 95% CI: 1.29, 3.76), obesity (aOR: 1.94, 95% CI: 1.13, 3.32) and rheumatoid arthritis (aOR: 7.73, 95% CI: 4.47, 13.36) were also independently associated with arthralgia after adjustment for confounders. CONCLUSIONS: Arthralgia in midlife Singaporean women was associated with menopausal symptoms of vaginal dryness and physical and mental exhaustion. Women with poor muscle strength were more likely to experience menopausal arthralgia.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".