The relationship between reductions in knee loading and immediate pain response whilst wearing lateral wedged insoles in knee osteoarthritis
Bibliographic record
Abstract
Studies of lateral wedge insoles (LWIs) in medial knee osteoarthritis (OA) have shown reductions in the average external knee adduction moment (EKAM) but no lessening of knee pain. Some treated patients actually experience increases in the EKAM which could explain the overall absence of pain response. We examined whether, in patients with painful medial OA, reductions in the EKAM were associated with lessening of knee pain. Each patient underwent gait analysis whilst walking in a control shoe and two LWI's. We evaluated the relationship between change in EKAM and change in knee pain using Spearman Rank Correlation coefficients and tested whether dichotomizing patients into biomechanical responders (decreased EKAM) and non-responders (increased EKAM) would identify those with reductions in knee pain. In 70 patients studied, the EKAM was reduced in both LWIs versus control shoe (-5.21% and -6.29% for typical and supported wedges, respectively). The change in EKAM using LWIs was not significantly associated with the direction of knee pain change. Further, 54% were biomechanical responders, but these persons did not have more knee pain reduction than non-responders. Whilst LWIs reduce EKAM, there is no clearcut relationship between change in medial load when wearing LWIs and corresponding change in 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 | 0.001 | 0.006 |
| 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) | 0.002 | 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".