Unstable Shoe Construction and Reduction of Pain in Osteoarthritis Patients
Bibliographic record
Abstract
PURPOSE: The purposes of this study were to assess a) the effectiveness of Masai Barefoot Technology (MBT) shoe in reducing knee pain in persons with knee osteoarthritis (OA) and (b) changes in balance, ankle and knee ROM, and ankle strength compared with a high-end walking shoe for 12 wk. METHODS: The research design was a randomized controlled trial (123 subjects, knee OA). Subjects were randomized to a MBT (N = 57) or a control shoe (N = 66). A Western Ontario and McMaster Universities (WOMAC) OA index, BMI, balance, active ROM, and ankle torque were quantified at week 0, 3, 6, 9, and 12. Two-sample t-tests were done for between-group comparisons. RESULTS: There was no significant difference between groups in total pain score. A significant reduction over the 12-wk period was found for both shoe conditions (-42/500 or 25.6% MBT, -46.2 or 27.1% control). There was no significant group difference in pain during walking (t = -1.09, P = 0.28). Pain during walking was significantly reduced by 5.2/100 mm in the MBT and 9.7/100 mm in the control group. Total pain showed a significant reduction for the MBT -27.4/500 (-16.6%) and the control group -28.9/500 (-17.0%) between baseline and week 3. Between week 3 and 6, there was a significant reduction for the MBT group only (-27.2/500 or -20.0%). There was a significant increase in the static balance between baseline and 12 wk in the MBT group only, although the difference between groups was not significant. DISCUSSION: The results indicate that special shoe interventions can reduce pain in subjects with moderate knee OA.
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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 | 0.001 |
| 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".