Long Term Effect of Weight Change on Clinical Manifestation of Knee Osteoarthritis among Women Treated with Orlistat
Bibliographic record
Abstract
Background: Combined therapy of weight loss and exercise provide today the best treatment for Knee Osteoarthritis. Most studies evaluating the effect of this therapy focus on the active intervention phase or immediately after the end of the treatment. Objectives: To describe the of long term effectiveness of weight loss and exercise treatment on Knee Osteoarthritis. Methods: Medical charts of 10 overweight women who suffered from Knee Osteoarthritis and had been treated for six months with a combined treatment of weight loss with Orlistat in the recommended dose, aerobic exercise and muscle mass strengthening, were retrospectively reviewed. Knee Osteoarthritis symptoms were assessed before treatment, at the end of treatment and following 6 months using the Western Ontario and McMaster Universities Osteoarthritis Index. Results: In comparison to baseline, we documented a statistically significant improvement in pain, stiffness, and function at the end of the treatment (37.0 vs. 21.0, P=0.007, 44.5 vs. 28.3, P=0.037 and 45.5 vs. 27.1, P=0.005, respectively) together with reduction in BMI (32.9 vs. 29.5, P=0.007). Six months later, although the mean BMI had returned to its baseline (31.1, P=0.126), the improvement in all parameters still existed (23.9, P=0.028, 27.1, P=0.028 and 32.9, P=0.037, respectively). Conclusions: Weight reduction together with muscle strengthening can improve function, stiffness, and pain symptoms in women with knee Osteoarthritis. Our case series suggest that this combined intervention may potentially maintain clinical improvement even when patients return to their baseline weight.
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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 | 0.003 |
| 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".