Effect of Combined Exercise Program on Lower Extremity Alignment and Knee Pain in Patients with Genu Varum
Bibliographic record
Abstract
This study aimed to assess the effect of a combined exercise program on lower-extremity alignment and knee pain in patients with genu varum. Forty-seven middle-aged women with knee pain and genu varum were randomly divided into the exercise (EG, n = 24) and control (CG, n = 23) groups. The exercise group underwent a combined exercise program lasting 60 min in one session, three times a week for 12 weeks. Knee-to-knee length (KTKL), hip-knee-ankle angle (HKAA), hip inclination angle (HIA), and medial proximal tibial angle (MPTA) were assessed to evaluate lower-extremity alignment. To evaluate knee pain, the short form-McGill Pain Questionnaire (SF-MPQ) were used. There was a significant difference between the groups, and a decrease of 16% in KTKL (from 6.48 ± 1.26 cm to 5.47 ± 1.21 cm) was shown in EG. Other variables, including HKAA, HIA, and MPTA on the right side, showed significant differences between pre- and post-intervention in EG (p < 0.01, p < 0.01, and p < 0.01, respectively). SF-MPQ score improved with 45% from 18.75 ± 1.64 to 10.33 ± 2.47 after exercise intervention in EG. These results suggest that the combined exercise program, including strength and neuromuscular exercises, is an effective intervention for improving lower-extremity alignment and knee pain in middle-aged women with genu varum.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".