Lateral wedge insole for knee osteoarthritis: randomized clinical trial
Bibliographic record
Abstract
CONTEXT AND OBJECTIVE: Optimal management of knee osteoarthritis requires a combination of pharmacological and non-pharmacological methods. The use of lateral wedge insoles to treat medial knee osteoarthritis is recommended, but there is still controversy about its efficacy. The purpose of this study was to ascertain whether the use of lateral wedge insoles can diminish pain and improve function in patients with medial knee osteoarthritis. DESIGN AND SETTING: Prospective randomized trial conducted in a tertiary-level hospital. METHODS: We prospectively enrolled 58 patients with medial knee osteoarthritis and randomized them to use either a lateral wedge insole with subtalar strapping (Group W), or a neutral insole with subtalar strapping (Group N - control). All the patients were instructed to use the insole for five to ten hours per day. A visual analogue pain scale, the Western Ontario and McMaster Universities Arthritis Index (WOMAC) and the Lequesne questionnaire were applied at baseline and at weeks 2, 8 and 24. RESULTS: At weeks 8 and 24, both groups showed lower scores for WOMAC (P = 0,023 and P = 0,012 respectively). There were no statistically significant differences between the groups regarding the visual analogue pain scale, WOMAC or Lequesne results at any time evaluated. CONCLUSION: The use of a lateral wedge insole with subtalar strapping improved the patients' symptoms and function but was not superior to placebo insoles.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".