Influence of a Full-Body Compression Suit on Trunk Positioning and Knee Joint Mechanics During Lateral Movements
Bibliographic record
Abstract
Trunk positioning has been shown to be associated with knee joint loading during athletic tasks, especially changes of direction. The purpose of the present study was to test whether a full-body compression suit (FBCS) would improve trunk positioning and knee joint control during lateral movements. Twelve female athletes performed lateral reactive jumps (LRJ) and unanticipated cuttings with and without the customized FBCS, while 3D kinematics and kinetics were measured. FBCS did not influence trunk positioning during LRJ and led to increased trunk lateral lean during cuttings (P < .001). However, while wearing FBCS, knee joint abduction and internal rotation angles were reduced during LRJ (P < .001 and P = .013, respectively), whereas knee joint moments were comparable during cuttings. FBCS cannot support the trunk segment during unanticipated dynamic movements. But, increased trunk lateral lean during cutting maneuvers was not high enough to elicit increased knee joint moments. On the contrary, knee joint abduction and internal rotation were reduced during LRJ, speaking for a better knee joint alignment with FBCS. Athletes seeking to improve trunk positioning may not benefit from a FBCS.
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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".