Improved WOMAC Score Following Treatment with Nanoparticle Phyllanthus Amarus Phonophoresis Gel for Knee Osteoarthritis
Bibliographic record
Abstract
Objective: The aim of this study is to compare the effects of phonophoresis of nanoparticle Phyllanthus amarus phonophoresis gel (PP) and ultrasound therapy (US) in patients with osteoarthritis of knee (OA). Method: OA knee patients (n=40) who had symptomatic knee pain were randomly allocated into 2 groups. There are US group (ultrasound group) and PP group (Phyllanthus amarus gel phonopheresis group)(20 in each group) were treated with an ultrasound program by using continuous mode, 1.0 W/cm 2 , 10 minutes per session, once daily and10 sessions. The nanoparticle Phyllanthus amarus gel was used in the PP group, while the US group was use the nondrug coupling gel. Pain and function assessment were measured by the visual analog scale (VAS) and the Western Ontario and McMaster Universities O-osteoarthritis Index (WOMAC), respectively.Moreover, the range of motion was used to measureby goniometers. Three primary outcomes were investigated before and after treatment 10 sessions. Results: The VAS and total WOMAC scores were significantly increasedpost-treatment in both groups (P < 0.001). The PP group showed more significant improve VAS and total WOMAC scores than the US group (P<.001). However, ROM was not significant in both groups to compare to baseline. Conclusion: The nanoparticle Phyllanthus amarus phonophoresis gelwas significantly reduced pain and improve knee functioning. Itis suggested as a new, effective method for treatment OA knee for relieving pain and improving function.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".