A comparison of bone quality at the distal radius between Asian and white adolescents and young adults: An HR-pQCT study
Bibliographic record
Abstract
Paradoxically, Asians have lower areal bone mineral density (aBMD), but their rates of hip and wrist fractures are lower than whites. Therefore, we used high-resolution pQCT (HR-pQCT) to determine whether differences in bone macrostructure and microstructure, BMD, and bone strength at the distal radius were apparent in Asian (n = 91, 53 males, 38 females, [mean ± SD] 17.3 ± 1.5 years) and white (n = 89, 46 males, 43 females, 18.1 ± 1.8 years) adolescents and young adults. HR-pQCT outcomes included total BMD (Tt.BMD), trabecular bone volume fraction (BV/TV), and trabecular number (Tb.N), thickness (Tb.Th), and separation (Tb.Sp). We used an automated segmentation algorithm to determine total bone area (Tt.Ar), and cortical BMD (Ct.BMD), porosity (Ct.Po), and thickness (Ct.Th), and we applied finite element (FE) analysis to HR-pQCT scans to estimate bone strength. We fit sex-specific multivariable regression models to compare bone outcomes between Asians and whites, adjusting for age, age at menarche (girls), lean mass, ulnar length, dietary calcium intake, and physical activity. In males, after adjusting for covariates, Asians had 11% greater Tt.BMD, 8% greater Ct.BMD, and 25% lower Ct.Po than whites (p < 0.05). Also, Asians had 9% smaller Tt.Ar and 27% greater Ct.Th (p < 0.01). In females, Asians had smaller Tt.Ar than whites (16%, p < 0.001), but this difference was not significant after adjusting for covariates. Asian females had 5% greater Ct.BMD, 12% greater Ct.Th, and 11% lower Tb.Sp than whites after adjusting for covariates (p < 0.05). Estimated bone strength did not differ between Asian and white males or females. Our study supports the notion of compensatory elements of bone structure that sustain bone strength; smaller bones as observed between those of Asian origin compared with white origin have, on average, more dense, less porous, and thicker cortices. Longitudinal studies are needed to determine whether ethnic differences in bone structure exist in childhood, persist into old age, and whether they influence fracture risk.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".