Bone microstructure at the distal tibia provides a strength advantage to males in late puberty: An HR-pQCT study
Bibliographic record
Abstract
Bone is a complex structure with many levels of organization. Advanced imaging tools such as high-resolution (HR) peripheral quantitative computed tomography (pQCT) provide the opportunity to investigate how components of bone microstructure differ between the sexes and across developmental periods. The aim of this study was to quantify the age- and sex-related differences in bone microstructure and bone strength in adolescent males and females. We used HR-pQCT (XtremeCT, Scanco Medical, Geneva, Switzerland) to assess total bone area (ToA), total bone density (ToD), trabecular bone density (TrD), cortical bone density (CoD), cortical thickness (Cort.Th), trabecular bone volume (BV/TV), trabecular number (Tb.N), trabecular thickness (Tb.Th), trabecular separation (Tb.Sp), trabecular spacing standard deviation (Tb.Sp SD), and bone strength index (BSI, mg2/mm4) at the distal tibia in 133 females and 146 males (15 to 20 years of age). We used a general linear model to determine differences by age- and sex-group and age x sex interactions (p<0.05). Across age categories, ToD, CoD, Cort.Th, and BSI were significantly lower at 15 and 16 years compared with 17 to 18 and 19 to 20 years in males and females. There were no differences in ToA, TrD, and BV/TV across age for either sex. Between sexes, males had significantly greater ToA, TrD, Cort.Th, BV/TV, Tb.N, and BSI compared with females; CoD and Tb.Sp SD were significantly greater for females in every age category. Males' larger and denser bones confer a bone-strength advantage from a young age compared with females. These structural differences could represent bones that are less able to withstand loads in compression in females.
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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 | 0.000 |
| 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".