Functional knee brace use for 21 h leads to a longer duration to achieve peak vertical ground reaction forces and the removal of the brace after 17.5 h results in faster loading of the knee joint
Bibliographic record
Abstract
PURPOSE: To investigate the landing strategies used after discontinuing and continuing the use of a functional knee brace (FKB) while performing a drop jump. METHODS: Following published methodology and power analysis, 23 uninjured male athletes, mean age of 19.4 ± 3.0 years, performed seven tests, during three test conditions (nonbraced, braced and removed brace or continued brace use), over 6 days of 12 testing sessions (S) for a total of 38.5 h. Each subject was provided with a custom-fitted FKB. This study focuses on the single leg drop jump kinetics during S12 when subjects were randomly selected to remove the FKB after 17.5 h or continued use of FKB. The time to peak vertical ground reaction forces (PVGRF) and PVGRF were recorded on landing in eight trials. RESULTS: After brace removal, a significantly shorter mean time to PVGRF was recorded (9.4 ± 22.9 msec (3.9%), p = 0.005, 95% confidence interval (95% CI): -168.1, 36.1), while continued brace use required a nonsignificant (n.s.) longer mean duration to achieve PVGRF (19.4 ± 53.6 msec (8.9%), n.s., 95% CI: -49.7, 73.4). No significant mean PVGRF difference was found in brace removal (25.3 ± 65.8 N) and continued brace use (25.1 ± 23.0 N). CONCLUSION: Removal of FKB after 17.5 h of use led to a significantly shorter time to achieve PVGRF, while continued brace use for 21 h required a longer duration to achieve PVGRF, suggesting faster and slower knee joint loading, respectively. Understanding the concerns associated with the use of FKB and the kinetics of the knee joint will assist clinicians in counselling athletes about the risks and benefits of using an FKB. LEVEL OF EVIDENCE: Level II.
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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".