Therapeutic Effectiveness and Safety of Mesotherapy in Patients with Osteoarthritis of the Knee
Bibliographic record
Abstract
Objective. To evaluate the therapeutic effectiveness and safety of mesotherapy by comparing it with the classic systematic therapy in patients with osteoarthritis (OA). Methods. Sixty patients were included and classified into two groups based on the existence of contraindications for nonsteroidal anti‐inflammatory drugs (NSAIDs). These patients were treated with oral NSAIDs (Group A) or mesotherapy (Group B). After completing the treatment, the patients were followed up for 6 months. Their clinical features, laboratory results, and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores were evaluated. Results. A total of 50 patients completed treatment and follow‐up. The patients in Group B had significantly fewer gastric acid‐related complaints and requested less supplementary treatment for recurrent pain (p < 0.05). The patients in both groups exhibited decreased blood viscosity after treatment (p < 0.05). WOMAC scores, specifically those for pain and stiffness, were found to be significantly improved after either type of treatment (p < 0.05). Mesotherapy also ameliorated physical function (p < 0.05). Furthermore, Group B presented with better outcomes than Group A (p < 0.05 or p < 0.01). Conclusion. Our results suggest that mesotherapy is an effective and safe treatment for patients with OA. Clinicians should consider mesotherapy as an alternative therapy for patients with contraindications for NSAID use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".