A multi‐method assessment of bone maintenance and loss in an<scp>I</scp>mperial<scp>R</scp>oman population: Implications for future studies of age‐related bone loss in the past
Bibliographic record
Abstract
OBJECTIVES: One of the hallmarks of contemporary osteoporosis and bone loss is dramatically higher prevalence of loss and fragility in females post-menopause. In contrast, bioarchaeological studies of bone loss have found a greater diversity of age- and sex-related patterns of bone loss in past populations. We argue that the differing findings may relate to the fact that most studies use only a single methodology to quantify bone loss and do not account for the heterogeneity and complexity of bone maintenance across the skeleton and over the life course. METHODS: We test the hypothesis that bone mass and maintenance in trabecular bone sites versus cortical bone sites will show differing patterns of age-related bone loss, with cortical bone sites showing sex difference in bone loss that are similar to contemporary Western populations, and trabecular bone loss at earlier ages. We investigated this hypothesis in the Imperial Roman population of Velia using three methods: radiogrammetry of the second metacarpal (N = 71), bone histology of ribs (N = 70), and computerized tomography of trabecular bone architecture (N = 47). All three methods were used to explore sex and age differences in patterns of bone loss. RESULTS: The suite of methods utilized reveal differences in the timing of bone loss with age, but all methods found no statistically significant differences in age-related bone loss. DISCUSSION: We argue that a multi-method approach reduces the influence of confounding factors by building a reconstruction of bone turnover over the life cycle that a limited single-method project cannot provide. The implications of using multiple methods beyond studies of bone loss are also discussed.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".