Association between the lower extremity biomechanical factors with osteoarthritis of knee
Bibliographic record
Abstract
Introduction: Osteoarthritis (OA) of the knee joint is one of the causes of pain and physical disability. Our aim is to prevent the OA of the knee joint. Hence, to prevent and to treat, the OA knee pain needs to find an association between various biomechanical factors of the lower limb and OA knee pain. Therefore, to assess the association between the lower extremity biomechanical factors with osteoarthritis of knee pain. Materials and Methods: Our study was a cross-sectional study, in that we have taken fifty participants who already diagnosed OA. The various biomechanical factors of the lower limb were measured along with outcome scales such as the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) for the OA knee and Numeric Rating Scale (NRS) for the knee pain from each participant. Results: There was a significant correlation found between femoral anteversion and navicular drop with WOMAC scale with a P = 0.001 and 0.03, respectively. The significant correlation between femoral anteversion, hamstring muscle length, Q angle (dynamic), and tibial torsion with NRS pain scale with P = 0.07, 0.06, 0.07, and 0.06, respectively. Conclusion: The study concluded that body mass index, femoral anteversion, hamstring's length, navicular drop, tibial torsion, and Q angle (dynamic) are various biomechanical factors which might be responsible for the incidence of the OA knee along with functional limitation and OA knee pain.
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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 | 0.001 |
| 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) | 0.004 | 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".