Aquatic versus land-based exercise for knee osteoarthritis: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Whether land- or aquatic-based rehabilitation is more effective in improving knee osteoarthritis (OA) is still unclear. This study assessed the effectiveness of aquatic-based treatments in patients with knee OA. METHODS: The participants were divided into a land-based exercise group (G1, n=30) and a water-based exercise group (G2, n=30). The exercises were performed for 8 weeks. The primary endpoint was a response to physical therapy, defined as a 20% decrease in the summed score for the Western Ontario and McMaster Universities-Osteoarthritis Index (WOMAC) pain subscale from T1 (before the start of the rehabilitation program) to T2 (8 weeks later). The secondary endpoints included the Visual Analog Scale (VAS) for pain, WOMAC functional and stiffness subscales, Lequesne Index, and Medical Outcome Study Short Form (SF-12) for physical and mental health. RESULTS: A 20% decrease in the summed WOMAC pain subscale score was noted in 33% of patients in G1 (n=10) and 93% in G2 (n=28) (P<0.001). VAS scores at walking decreased by 14% in G1 vs. 37% in G2 (P<0.001), WOMAC stiffness subscale decreased by 18% in G1 vs. 53% in G2 (P<0.001), and the Lequesne index decreased by 10% in G1 vs. 33% in G2 (P<0.001). Quality of life improvement was greater in G2 than in G1; SF-12 (physical) increased by 2.3 in G1 vs. 5.4 in G2 (P=0.023), and SF-12 (mental) increased by 6.3 in G1 vs. 10.9 in G2 (P=0.022). CONCLUSION: Both aquatic and land-based exercises improved pain intensity, functional impairment, degree of handicap, and quality of life impairment caused by OA. However, the improvement was more significant in the aquatic-based exercises group.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".