pQCT bone geometry and strength: population epidemiology and concordance in Australian children aged 11–12 years and their parents
Bibliographic record
Abstract
OBJECTIVES: To describe the epidemiology and concordance of bone health in a population-based sample of Australian parent-child dyads at child age 11-12 years. DESIGN: Population-based cross-sectional study (the Child Health CheckPoint) nested between waves 6 and 7 of the Longitudinal Study of Australian Children (LSAC). SETTING: Assessment centres in seven cities around Australia, February 2015-March 2016. PARTICIPANTS: of all participating CheckPoint families (n=1874), bone data were available for 1222 dyads (1271 children, 50% girls; 1250 parents, 86% mothers). OUTCOME MEASURES: Peripheral quantitative CT (pQCT) of the non-dominant leg scanned at the 4% (distal) and 66% (mid-calf) tibial sites. Stratec XCT 2000 software generated estimates of bone density, geometry and polar stress-strain index.Parent-child concordance were assessed using Pearson's correlation coefficients and multivariable linear regression models. Percentiles were determined using survey weights. Survey weights and methods accounted for LSAC's complex sampling, stratification and clustering within postcodes. RESULTS: Concordances were greater for the geometric pQCT parameters (periosteal circumference 0.38, 95% CI 0.33 to 0.43; endosteal circumference 0.42, 95% CI 0.37 to 0.47; total cross-sectional area 0.37, 95% CI 0.32 to 0.42) than density (cortical density 0.25, 95% CI 0.19 to 0.30). Mother-child and father-child values were similar. Relationships attenuated only slightly on adjustment for age, sex and body mass index. Percentiles and concordance are presented for the whole sample and by sex. CONCLUSIONS: There is strong parent-child concordance in bone geometry and, to a lesser extent, density even before the period of peak adolescent bone deposition. This geometrical concordance suggests that future intergenerational bone studies could consider using pQCT rather than the more commonly used dual X-ray absorptiometry (DXA).
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".