The efficacy and safety of hydrotherapy in patients with knee osteoarthritis: a meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Currently, there is poor evidence of the effect of hydrotherapy on patients with knee osteoarthritis (OA). The authors performed a meta-analysis from randomized controlled trials to determine the efficacy and safety of a hydrotherapy program on measures of pain and knee function in individuals living with knee OA. METHODS: A literature review included PubMed, EMBASE, Cochrane Library, Science Citation Index, ScienceDirect, and Ovid. Studies evaluating the efficacy of hydrotherapy for knee OA up to August 2023 were included. The research was reported based on the preferred reporting items for systematic reviews and meta-analysis guidelines to ensure the reliability and verity of results. Statistical analysis was performed using Stata/SE version 15.0. RESULTS: A total of six randomized controlled trials were included for data extraction and meta-analysis. The present study revealed that there were significant differences between the two groups regarding the pain intensity at 1 week (WMD=-0.429; 95% CI: -0.679 to -0.179; P =0.001), 4 week (WMD=-0.308; 95% CI: -0.587 to -0.030; P =0.030) and 8 week (WMD=-0.724; 95% CI: -1.099 to -0.348, P <0.001). Furthermore, hydrotherapy was associated with improved outcome of the Western Ontario and McMaster Universities Arthritis index at 1 week (WMD=-3.314; 95% CI: -6.484 to -0.145, P =0.040), 4 week (WMD= -3.630; 95% CI: -6.893 to -0.366, P =0.029) and 8 week (WMD=-3.775; 95% CI: -7.315 to -0.235; P =0.037). No serious adverse events were observed in all patients who received hydrotherapy. CONCLUSION: Hydrotherapy is efficacious and safe for reducing pain and improving functional status in individuals with knee OA, without increasing the risk of adverse effects.
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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 | 0.027 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.059 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".