Six‐week gait retraining program reduces knee adduction moment, reduces pain, and improves function for individuals with medial compartment knee osteoarthritis
Bibliographic record
Abstract
This study examined the influence of a 6-week gait retraining program on the knee adduction moment (KAM) and knee pain and function. Ten subjects with medial compartment knee osteoarthritis and self-reported knee pain participated in weekly gait retraining sessions over 6 weeks. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores and a 10-point visual-analog pain scale score were measured at baseline, post-training (end of 6 weeks), and 1 month after training ended. Gait retraining reduced the first peak KAM by 20% (p < 0.01) post-training as a result of a 7° decrease in foot progression angle (i.e., increased internal foot rotation), compared to baseline (p < 0.01). WOMAC pain and function scores were improved at post-training by 29% and 32%, respectively (p < 0.05) and visual-analog pain scale scores improved by two points (p < 0.05). Changes in WOMAC pain and function were approximately 75% larger than the expected placebo effect (p < 0.05). Changes in KAM, foot progression angle, WOMAC pain and function, and visual-analog pain score were retained 1 month after the end of the 6-week training period (p < 0.05). These results show that a 6-week gait retraining program can reduce the KAM and improve symptoms for individuals with medial compartment knee osteoarthritis and 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 | 0.000 |
| 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) | 0.001 | 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".