Bone Microarchitecture Changes in Peri- and Post-Menopausal Women: Cortical Porosity is a Marker for Accelerated Change During Menopause
Bibliographic record
Abstract
During menopause, accelerated bone loss occurs, accompanied by bone microarchitecture changes. This has primarily been characterized with cross sectional dual x-ray absorptiometry (DXA). Our longitudinal study aims to compare rate of change (ROC) in bone microarchitectural parameters in peri- and post-menopausal women using high resolution peripheral quantitative computed tomography (HR-pQCT).Participants were selected from the Calgary Canadian Multicentre Osteoporosis Study (CaMos) from menopause information provided by questionnaire (stage 1-5). The peri-menopause group (n=26) includes women beginning menopause transition to nearing the end (stage 2-4). The post-menopause group (n=65) had completed the midlife process (stage 5). HR-pQCT (Scanco Medical, Switzerland) non-dominant radius and left tibia scans provided baseline and follow-up (average=5.5yrs) assessments of bone mineral density, total area (Tt.Ar) and cortical porosity (Ct.Po). To compare repeat scans, automated 3D image registration was conducted (IPL software). T-tests compared differences between groups.Both groups lost radius and tibia bone density. ROC between groups was not different at the radius. In contrast, tibia Tt.Ar and Ct.Po ROC differed. Peri-menopausal women experienced accelerated Ct.Po change compared to post-menopausal women (9.0 and 6.3%/yr respectively, p=0.046) and less Tt.Ar change (0.06 and 0.13%/yr, p=0.017). Increased ROC in Ct.Po observed in peri-menopausal women is consistent with data showing increased bone remodeling during menopause, with inadequate repair. Similarly, Tt.Ar is known to increase with age, as observed in both groups, but appears to occur at a faster rate post-menopause. This is the first longitudinal study at high resolution to investigate longitudinal microarchitecture changes during and after menopause.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".