Peak bone mass from longitudinal data: Implications for the prevalence, pathophysiology, and diagnosis of osteoporosis
Bibliographic record
Abstract
We estimated peak bone mass (PBM) in 615 women and 527 men aged 16 to 40 years using longitudinal data from the Canadian Multicentre Osteoporosis Study (CaMos). Individual rates of change were averaged to find the mean rate of change for each baseline age. The age range for PBM was defined as the period during which bone mineral density (BMD) was stable. PBM was estimated via hierarchical models, weighted according to 2006 Canadian Census data. Lumbar spine PBM (1.046 ± 0.123 g/cm(2)) occurred at ages 33 to 40 years in women and at 19 to 33 years in men (1.066 ± 0.129 g/cm(2)). Total hip PBM (0.981 ± 0.122 g/cm(2)) occurred at ages 16 to 19 years in women and 19 to 21 years in men (1.093 ± 0.169 g/cm(2)). Analysis of Canadian geographic variation revealed that the levels of PBM and of mean BMD in those over age 65 sometimes were discordant, suggesting that PBM and subsequent rates of bone loss may be subject to different genetic and/or environmental influences. Based on our longitudinally estimated PBM values, the estimated Canadian prevalences of osteoporosis (T-score < -2.5) were 12.0% (L(1)-L(4)) and 9.1% (total hip) in women aged 50 years and older and 2.9% (L(1)-L(4)) and 0.9% (total hip) in men aged 50 years and older. These were higher than prevalences using cross-sectional PBM data. In summary, we found that the age at which PBM is achieved varies by sex and skeletal site, and different reference values for PBM lead to different estimates of the prevalence of osteoporosis. Furthermore, lack of concordance of PBM and BMD over age 65 suggests different determinants of PBM and subsequent bone loss.
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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".