Women’s Mid-Life Night Sweats and 2-Year Bone Mineral Density Changes: A Prospective, Observational Population-Based Investigation from the Canadian Multicentre Osteoporosis Study (CaMos)
Bibliographic record
Abstract
Women’s hot flushes and night sweats, collectively called vasomotor symptoms (VMS), are maximal (79%) in late perimenopause. The evidence describing whether VMS are associated with loss of areal bone mineral density (BMD) is mixed. We examined baseline and 2-year data for 1570 randomly selected women aged 43–63 in the Canadian Multicentre Osteoporosis Study (CaMos), a prospective Canada-wide study; we used linear regression to assess the relationship of night sweats (VMSn) with BMD and its changes. Clinically important VMSn occurred for 12.2%. Women with VMSn were slightly younger (54.5 vs. 55.3 years, p = 0.02) and less likely to use sex steroid therapies (39.8% vs. 51.4%, p < 0.05). BMD at the lumbar spine (L1-4), femoral neck (FN) and total hip (TH) were similar between those with/without VMSn. In adjusted models, we did not find a significant association between VMSn and 2-year change in L1-4, FN and TH BMD. Age, reproductive status, weight, sex steroid therapy and smoking status were associated with 2-year change in BMD. Incident fractures over 2 years also did not differ by VMSn. Our analyses were restricted to VMSn and may not truly capture the relationship between VMS and BMD. Additional research involving VMS, bone loss and fracture incidence is needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".