Torque Strength Targets: Sex and Sport Differences in Knee and Shoulder Profiles Among Amateur Athletes
Bibliographic record
Abstract
OBJECTIVE: To describe knee and shoulder strength characteristics across amateur sports and compare strength across sexes and sports groups. METHODS: A total of 233 healthy athletes who were competing at national or international levels across 27 different sports (106 males and 127 females) participated in this study. Using a Con-TREX MJ system, we assessed peak and relative torque, and rate of force development at 200 ms (RFD 200 ), in knee and shoulder joint muscles. Differences between sexes were assessed using Student’s t tests and Cohen’s d for effect sizes; nonparametric MANOVA (multivariate analysis of variance) compared sports groups. Spearman rank correlations were used to compare average peak torque and RFD 200 . RESULTS: Male athletes had greater peak and relative torques than female athletes ( P<.02, d = 0.33-1.87). Significant differences across torque variables were also observed between combat, team, precision/skill, and speed/strength sport groups for the knee ( P<.01, R 2 = 0.15) and the shoulder ( P<.01, R 2 = 0.03) measurements. Mean peak torque and RFD 200 were highly correlated for knee flexion and extension (r = 0.95 and 0.97, respectively), and for shoulder external and internal rotation (r = 0.95 and 0.99, respectively). CONCLUSIONS: There were sex and group differences in knee flexion and extension and shoulder internal and external rotation. Male participants were stronger, and athletes from the speed/strength groups showed the greatest values for knee strength, whereas combat sports athletes demonstrated the greatest shoulder rotation strength. JOSPT Open 2025;3(2):137-145. Epub 17 January 2025. doi:10.2519/josptopen.2025.0095
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".