Proprioception of Knee Joint in Athletes and Non-Athletes Obese
Bibliographic record
Abstract
BACKGROUND: Proprioception plays an integral role in the neuromotor control of the knee joint. The objective of this study was to evaluate the relationship of knee joint proprioception deficit and regular exercise activities with obesity. MATERIAL/METHOD: In this case control study, we had 60 participants, aged 18 to 35 in four groups (15 athletes obese; 15 athletes with normal weight; 15 non-athletes obese; 15 non-athletes with normal weight). The average Body Mass Index for the obese groups was 33.50±(3.10) kg/m2 and for the normal weight groups was 23.77±(2.94) kg/m2. We used a Biodex Multi-Joint System 4 Isokinetic Dynamometer to examine proprioception acuity as the amount of a subject's error when trying to reproduce a test knee extension angle (a measure of the joint position sense). We tested proprioception actively (active reproduction test; AAR) and passively (passive reproduction test; PAR) in the right leg. RESULTS: The non-athlete obese group had significantly poorer proprioception acuity in the knee extension movement compared to the normal weight groups and also compared to the athlete obese group. For knee active angle reproduction test AAR, a significant difference was found (p=0.011). The results for passive angle reproduction test PAR, revealed no significant differences between the four groups (p=0.596). CONCLUSIONS: The obese groups showed a deficit in the proprioception function in knee extension movement. Furthermore, the findings suggest that doing regular weight bearing training is associated with better proprioceptive function, even in obese groups. It could manifest that the deleterious effect of obesity on the knee joint proprioception might be stronger than the beneficial influence of exercise training.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".