Effect of a Compressive Garment on Kinematics of Jump-Landing Tasks
Bibliographic record
Abstract
de Britto, MA, Lemos, AL, dos Santos, CS, Stefanyshyn, DJ, and Carpes, FP. Effect of a compressive garment on kinematics of jump-landing tasks. J Strength Cond Res 31(9): 2480-2488, 2017-During jump-landing tasks, knee kinematics such as excessive valgus have been linked to knee injury in females. We determine the influence of a compressive garment on knee valgus during landing. Physically active women (n = 27, mean age 23 years) performed 4 different jump-landing tasks with 2 apparel conditions (compressive garment and regular sports shorts). Kinematic data were collected to determine knee flexion and valgus angles and the maximum jump height. Results showed that the compressive garment decreased knee flexion and knee valgus range of motion, without significant changes in the maximum jump height. As a practical application, we suggest that compression could be a strategy to reduce dynamic valgus without influencing jump performance, which motivates further study of its potential for knee injury prevention.
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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) | 0.001 | 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) | 0.003 | 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".