The Influence of Maturity Status on Muscle Architecture in School-Aged Boys
Bibliographic record
Abstract
PURPOSE: To determine the differences in muscle architecture of the lower limb in pre-peak height velocity (PHV), circa-PHV, and post-PHV boys. METHOD: Muscle architecture variables from both the gastrocnemius medialis (GM) and vastus lateralis (VL) were derived from ultrasonographic images in 126 school-aged boys. One-way analysis of variance using Bonferroni post hoc comparisons was employed to determine between-group differences, and effect sizes were calculated to establish the magnitude of these differences. RESULTS: All muscle architecture variables showed significant small to large increases from pre-PHV to post-PHV, excluding GM fascicle length (d = 0.59-1.39; P < .05). More discrete between-group differences were evident as GM and VL muscle thickness, and physiological thickness significantly increased between pre-PHV and circa-PHV (d > 0.57; P < .05); however, only the VL muscle thickness and physiological thickness increased from circa-PHV to post-PHV (d = 0.68; P < .05). The post-PHV group also showed larger GM pennation angles than the circa-PHV group (d = 0.59; P < .05). CONCLUSION: The combined results showed that maturation is associated with changes in muscle morphology. These data quantify that the maturity-related changes in muscle architecture variables provide a reference to differentiate between training-induced adaptations versus changes associated with normal growth and maturation.
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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".