Lateral wedges in knee osteoarthritis: What are their immediate clinical and biomechanical effects and can these predict a three‐month clinical outcome?
Bibliographic record
Abstract
OBJECTIVE: To assess immediate effects of laterally wedged insoles on walking pain, external knee adduction moment, and static alignment, and whether these immediate effects together with age, body mass index, and disease severity predict clinical outcome after 3 months of wearing insoles in medial knee osteoarthritis. METHODS: Forty volunteers (mean age 64.7 years, 16 men) were tested in random order with and without a pair of 5 degrees full-length lateral wedges. Immediate changes in static alignment were measured via radiographic mechanical axis and changes in adduction moment via 3-dimensional gait analysis. After 3 months of treatment with insoles, changes in pain and physical functioning were assessed via the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and patient-perceived global change scores. RESULTS: Reductions in the adduction moment occurred with insoles (first peak mean [95% confidence intervals (95% CI)] -0.22 [-0.28, -0.15] Nm/body weight x height %), accompanied by a reduction in walking pain of approximately 24% (mean [95% CI] -1.0 [-4.0, 2.0]). Insoles had no mean effect on static alignment. Mean improvement in WOMAC pain (P = 0.004) and physical functioning (mean [95% CI] -6 [-11, -1]) was observed at 3 months, with 25 (69%) and 26 (72%) of 36 individuals reporting global improvement in pain and functioning, respectively. Regression analyses demonstrated that disease severity, baseline functioning, and magnitude of immediate change in walking pain and the first peak adduction moment with insoles were predictive of clinical outcome at 3 months. CONCLUSION: Lateral wedges immediately reduced knee adduction moment and walking pain but had no effect on static alignment. Although some parameters predicted clinical outcome, these explained only one-third of the variance, suggesting that other unknown factors are also important.
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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 | 0.001 | 0.004 |
| 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) | 0.002 | 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".