Muscle Architecture and Maturation Influence Sprint and Jump Ability in Young Boys: A Multistudy Approach
Bibliographic record
Abstract
ABSTRACT: Radnor, JM, Oliver, JL, Waugh, CM, Myer, GD, and Lloyd, RS. Muscle Architecture and Maturation Influence Sprint and Jump Ability in Young Boys: A Multistudy Approach. J Strength Cond Res 36(10): 2741-2751, 2022-This series of experiments examined the influence of medial gastrocnemius (GM) and vastus lateralis (VL) muscle architecture (muscle thickness, pennation angle, and fascicle length) on sprint and jump performance in pre-, circa-, and post-peak height velocity (PHV) boys. In experiment 1, 1-way analysis of variance and Cohen's d effect sizes demonstrated that most muscle architecture measures were significantly greater in post-PHV compared with pre-PHV boys ( d = 0.77-1.41; p < 0.05). For most sprint and jump variables, there were small to moderate differences between pre-PHV to circa-PHV and circa-PHV to post-PHV groups ( d = 0.58-0.93; p < 0.05) and moderate to large differences between pre-PHV and post-PHV groups ( d = 1.01-1.47; p < 0.05). Pearson's correlation analyses in experiment 2 determined that muscle architecture had small to moderate correlations with sprint and jump performance ( r = 0.228-0.707, p < 0.05), with strongest associations within the post-PHV cohort. Chi-squared analyses in experiment 3 identified that, over 18 months, more POST-POST responders than expected made positive changes in GM and VL muscle thickness. Significantly more PRE-POST subjects than expected displayed changes in maximal sprint speed, while significantly more POST-POST individuals than expected showed positive changes in jump height. Muscle architecture seems to be larger in more mature boys compared with their less mature peers and likely underlies their greater performance in sprinting and jumping tasks. Boys experiencing, or having experienced, PHV make the largest increases in muscle architecture and sprinting and jumping performance when tracked over 18 months.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".