Soft tissues, areal bone mineral density and hip geometry estimates in active young boys: the PRO-BONE study
Bibliographic record
Abstract
PURPOSE: Soft tissues, such as fat mass (FM) and lean mass (LM), play an important role in bone development but this is poorly understood in highly active youths. The objective of this study was to determine whether FM or LM is a stronger predictor of areal bone mineral density (aBMD) and hip geometry estimates in a group of physically active boys after adjusting for height, chronological age, moderate-to-vigorous physical activity (MVPA), FM, and LM. METHODS: Participants included 121 boys (13.1 ± 1.0 years) from the PRO-BONE study. Bone mineral content (BMC) and aBMD were measured at total body, femoral neck and lumbar spine using dual-energy X-ray absorptiometry (DXA), and hip structural analysis was used to estimate bone geometry at the femoral neck. Body composition was assessed using DXA. The relationships of FM and LM with bone outcomes were analysed using simple and multiple linear regression analyses. RESULTS: Pearson correlation coefficients showed that total body (less head) aBMD was significantly correlated with LM but not FM. Multiple linear regression analyses showed that FM, after accounting for height, age, MVPA and LM had no significant relationship with aBMD or hip geometry estimates, except for arms aBMD. By contrast, there were positive associations between LM and most aBMD and hip geometry estimates, after accounting height, age, MVPA and FM. CONCLUSIONS: The results of this study suggest that LM, and not FM, is the stronger predictor of aBMD and hip geometry estimates in physically active boys. TRIAL REGISTRATION: ClinicalTrials.gov ISRCTN17982776.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".