A systematic review and meta-analysis of the effect of phonophoresis on patients with knee osteoarthritis
Bibliographic record
Abstract
Abstract This systematic review and meta-analysis investigated the effect of phonophoresis when various gel types were used. Medline (using PubMed), EMBASE, and Cochrane Central Register of Controlled Trials (CENTRAL) were used to search for relevant studies from the date of their inception to June 28, 2021. We included studies that were randomized controlled trials (RCTs), included patients with a diagnosis of knee osteoarthritis, included treatment with either phonophoresis or therapeutic ultrasound with placebo gel, and reported clinical and functional outcomes. Continuous variables are expressed as standardized mean differences (SMDs) with 95% confidence intervals (CIs). Statistical analysis was performed using RevMan 5.3 software. We initially retrieved 2176 studies and finally analyzed nine RCTs including 423 patients. The intervention group significantly outperformed the control group in pain scores with NSAID gel (SMD = − 0.53, 95% CI [− 1.02, − 0.05], I2 = 73%) and in the Western Ontario and McMaster Universities Arthritis Index (WOMAC) function score with corticosteroid gel (SMD = − 0.96, 95% CI [− 1.47, − 0.44], I2 = 20%). Phonophoresis alleviated pain and improved functional performance. Because of some limitations of this study, additional high-quality, large-scale RCTs are required to confirm the benefits.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.039 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".