The Effects of Brace Stiffness on Knee Joints During Pull-Up Jump Shot Movements in Amateur Female Basketball Players
Bibliographic record
Abstract
(1) Background: The pull-up jump shot is a commonly used scoring technique in basketball. This study aimed to investigate the biomechanical effects of knee brace stiffness on knee joint mechanics during the pull-up jump shot in female basketball players and to evaluate the potential risk of non-contact anterior cruciate ligament (ACL) injuries associated with different stiffness levels. (2) Methods: Sixty-six female basketball players performed pull-up jump shot drills while kinematic and kinetic data were collected using a Vicon motion capture system and a Kistler ground reaction force (GRF) plate. (3) Results: A one-way analysis of variance (ANOVA) revealed that both low-stiffness and high-stiffness knee braces significantly reduced knee flexion angles (p = 0.001) but increased indirect contact forces in the sagittal plane (p < 0.01). Notable differences were observed between low-stiffness and high-stiffness braces, as well as between braced and unbraced conditions. However, no significant differences were detected between the effects of low-stiffness and high-stiffness braces. (4) Conclusions: Athletes should select knee braces based on the intensity of competition and training, and those with ACL concerns should opt for high-stiffness knee braces for enhanced joint stability.
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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".