Lean mass and biological maturation as predictors of muscle power and strength performance in young athletes
Bibliographic record
Abstract
BACKGROUND: The biological maturation (BM) analyzed by peak height velocity (PHV) and bone age (BA), and lean body mass has been associated with the strength and muscle power of young athletes. However, the ability of BM (PHV and BA) and LM markers to predict muscle strength and power in young athletes remains uncertain. OBJECTIVE: The Aim was determine the predicting power of BM markers (PHV and BA) and LM in relation to muscle power of upper and lower limbs and muscle strength of upper limbs in adolescent athletes at puberty. METHODS: Ninety-two adolescent athletes (both sexes; age 12.4 ± 1.02 years) were assessed for body composition by dual-energy X-ray absorptiometry (DXA). Power of upper limbs (ULP), force handgrip (HG), vertical jump (VJ) and countermovement jump (CMJ) were recorded. BM was predicted by mathematical models to estimate PHV and BA. Multilayer artificial neural network analyses (MLP's) were used to determine the power of prediction of LM, PHV and BA on muscle power and strength of upper- and lower-limbs of the athletes. RESULTS: LM, BA and PHV were associated with HG (r>0.74, p<0.05) and ULS (r>0.60, p<0.05) in both sexes. In both sexes BA was associated with VJ (r>0.55, p<0.05) and CMJ (r>0.53, p<0.05). LM indicated associations (r>0.60, p<0.05) with BA and with PHV (r<0.83, p<0.05) in both sexes. MLP's analysis revealed that the LM provides > 72% of probability to predict the muscle power of upper- and lower-limbs, and the strength of the upper limbs; whereas PHV provides > 43% and bone age >64% in both female and male adolescent athletes. CONCLUSION: We identified that, like PHV and BA, LM is a strong predictor of low cost of both upper limbs muscle strength and upper and lower limbs power in adolescent athletes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".