Biological Maturation and Hormonal Markers, Relationship to Neuromotor Performance in Female Children
Bibliographic record
Abstract
Background: Mechanisms that influence muscle strength can interfere with neuromotor performance and overall health, thus hormone markers and maturation can interact in this process. Objective: The present study aimed to verify the relationship of hormonal markers and biological maturation on neuromotor abilities in young people. Methods: This is a cross-sectional study with 44 female participants (11.5 ± 1.5 years). Hormones were analyzed biochemically. Skeletal and somatic maturation were analyzed using anthropometry. The muscular power of the upper and lower limbs, body speed with change of direction, and speed of the upper limbs were verified. Results: Bone age was correlated with hormonal markers (estradiol: r = 0.58; p = 0.0007), (testosterone: r = 0.51; p = 0.005). Peak growth velocity correlated with estradiol (r = 0.51; p = 0.004). The power of the lower limbs (estradiol: r = 0.52; p = 0.006; testosterone: r = 0.42; p = 0.03) and of the upper limbs (estradiol: r = 0.51; p = 0.007; testosterone: r = 0.42; p = 0.02) had a positive correlation with hormone levels and had similar results with maturation. The analysis by artificial neural networks indicated that the maturation can predict the neuromotor performance between 57.4% and 76%, while the hormonal markers showed a potential of more than 95% for the foreshadowing of the neuromotor performance of the upper limbs. Conclusion: It was possible to conclude that the hormones had a relationship with maturational development and bone age in female subjects.
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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 | 0.000 | 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) | 0.002 | 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".