Aging Versus Postmenopausal Osteoporosis: Bone Composition and Maturation Kinetics at Actively-Forming Trabecular Surfaces of Female Subjects Aged 1 to 84 Years
Bibliographic record
Abstract
Bone strength depends on the amount of bone, typically expressed as bone mineral density (BMD), determined by dual-energy X-ray absorptiometry (DXA), and on bone quality. Bone quality is a multifactorial entity including bone structural and material compositional properties. The purpose of the present study was to examine whether bone material composition properties at actively-forming trabecular bone surfaces in health are dependent on subject age, and to contrast them with postmenopausal osteoporosis patients. To achieve this, we analyzed by Raman microspectroscopy iliac crest biopsy samples from healthy subjects aged 1.5 to 45.7 years, paired biopsy samples from females before and immediately after menopause aged 46.7 to 53.6 years, and biopsy samples from placebo-treated postmenopausal osteoporotic patients aged 66 to 84 years. The monitored parameters were as follows: the mineral/matrix ratio; the mineral maturity/crystallinity (MMC); nanoporosity; the glycosaminoglycan (GAG) content; the lipid content; and the pyridinoline (Pyd) content. The results indicate that these bone quality parameters in healthy, actively-forming trabecular bone surfaces are dependent on subject age at constant tissue age, suggesting that with advancing age the kinetics of maturation (either accumulation, or posttranslational modifications, or both) change. For most parameters, the extrapolation of models fitted to the individual age dependence of bone in healthy individuals was in rough agreement with their values in postmenopausal osteoporotic patients, except for MMC, lipid, and Pyd content. Among these three, Pyd content showed the greatest deviation between healthy aging and disease, highlighting its potential to be used as a discriminating factor.
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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 | 0.000 |
| 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) | 0.001 | 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".