The Effect of Strength Training on the Jump-Landing Biomechanics of Young Female Athletes
Bibliographic record
Abstract
OBJECTIVE: To investigate the effect of leg-focused strength training on the jump-landing mechanics of young female athletes. DESIGN: Single-blind, randomized controlled clinical trial. SETTING: University-based training program. PARTICIPANTS: Forty female athletes, 10 to 14 years old, were randomly allocated to intervention or active control. INTERVENTIONS: Twice weekly training was performed by the leg strengthening group [intervention group (IG); n = 19] and the active control group (CG; n = 17), for 12 weeks. Control group participants performed upper body strengthening exercises. MAIN OUTCOME MEASURE: Jump-landing performance was assessed by a blinded observer using the Landing Error Scoring System (LESS). RESULTS: There was no difference between the IG and CG postintervention (IG mean LESS score 6.0 ± SD 1.8 vs CG mean 6.1 ± SD 1.8; P = 0.85). CONCLUSIONS: Strength training of the legs does not seem to improve jump-landing abilities in young female athletes compared with active controls who strength-trained their arms. CLINICAL RELEVANCE: Leg strengthening may not provide an advantage over arm strengthening for improving jump-landing movement patterns in young female athletes. This has implications for the design of conditioning programs if injury prevention is a goal.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".