Lower Cortical Porosity and Higher Tissue Mineral Density in Chinese American Versus White Women
Bibliographic record
Abstract
Asian women have lower rates of hip and forearm fractures compared to other racial groups despite lower areal bone mineral density (aBMD). We have demonstrated microarchitectural differences, including greater cortical thickness (Ct.Th) and cortical volumetric BMD (Ct.BMD), in Chinese American versus white women. Yet it is not known whether greater Ct.BMD in Chinese American women is a result of greater tissue mineral density (TMD) or reduced cortical porosity (Ct.Po). Using an advanced segmentation algorithm based on high-resolution peripheral quantitative computed tomography (HR-pQCT) images, we tested the hypothesis that Chinese American women have better cortical skeletal integrity owing to lower Ct.Po and higher Ct.TMD compared with white women. A total of 78 Chinese American women (49 premenopausal and 29 postmenopausal) and 114 white women (46 premenopausal and 68 postmenopausal) were studied. Premenopausal Chinese American versus white women had greater Ct.Th, Ct.BMD, and Ct.TMD at both the radius and tibia, and decreased Ct.Po (p < 0.05). A similar pattern was observed between postmenopausal Chinese American and white women. As expected, postmenopausal versus premenopausal women had lower Ct.BMD at the radius and tibia in both races (p < 0.001). Ct.Po largely increased between premenopausal and postmenopausal women, whereas Ct.TMD decreased by 3% to 8% (p < 0.001) in both races. Age-related differences in Ct.Po and Ct.TMD did not differ by race. In summary, both reduced Ct.Po and greater Ct.TMD explain higher Ct.BMD in Chinese American versus white women. Thicker and preserved cortical bone structure in Chinese American women may contribute to greater resistance to fracture compared to white women.
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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 | 0.001 | 0.001 |
| 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".