Sex-, Ethnic-, and Age-Specific Centile Curves for pQCT- and HR-pQCT-Derived Measures of Bone Structure and Strength in Adolescents and Young Adults
Bibliographic record
Abstract
There are presently no adolescent centile curves for bone parameters at the tibial midshaft using peripheral quantitative computed tomography (pQCT) or at the distal radius and tibia using high-resolution pQCT (HR-pQCT). Thus, we aimed to develop sex-, ethnic-, site-, and age-specific centile curves for pQCT and HR-pQCT-derived bone outcomes for youth and young adults aged 10 to 21 years. We acquired pQCT scans (XCT3000 or XCT2000) at the tibial midshaft (50% site) and HR-pQCT scans (XtremeCT) at the distal radius (7% site) and tibia (8% site) in a convenience sample of participants in the mixed-longitudinal University of British Columbia Healthy Bones III Study. We scanned 778 10- to 21-year-olds annually for a maximum of 11 years using pQCT (413 girls, 56% Asian; 365 boys, 54% Asian; n = 3160 observations) and 349 10- to 21-year-olds annually for a maximum of 4 years using HR-pQCT (189 girls, 51% Asian; 165 boys, 50% Asian; n = 1090 observations). For pQCT, we report cortical bone mineral density (BMD), total bone cross-sectional area, and polar strength-strain index. For HR-pQCT, we report standard measures (total BMD, trabecular number, thickness, and bone volume fraction) and automated segmentation measures (total bone cross-sectional area, cortical BMD, porosity, and thickness). We applied finite element analysis to estimate failure load. We applied the lamda, mu, sigma (LMS) method using LMS ChartMaker Light (version 2.5, The Institute of Child Health, London, UK) to construct LMS tables and centile plots. We report sex- and age-specific centiles (3rd, 10th, 25th, 50th, 75th, and 97th) for whites and Asians for pQCT bone parameters at the tibial midshaft and HR-pQCT bone parameters at the distal radius and tibia. These centile curves might be used by clinicians and scientists to interpret values or better understand trajectories of bone parameters in clinical populations, those from different geographic regions or of different ethnic origins. © 2018 American Society for Bone and Mineral Research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".