Intra-articular injection of autologous fat tissue in the treatment of patients with chronic knee pain due to osteoarthritis
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to evaluate the safety and efficacy of autologous fat tissue injection into the knee joint for the treatment of osteoarthritis. METHODS: We reviewed 165 patients who received an intra-articular injection of autologous fat tissue for knee osteoarthritis. The efficacy of the treatment was evaluated at 1, 3, 6, and 12 months follow-up using the Visual Analogue Scale (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and Oxford Knee Score (OKS). Patients with knee arthritis were classified as grades I-IV according to the Kellgren-Lawrence scale (K-L). The clinical and demographic information of the patients, NSAIDs or opioid use, and the side effects related to the procedure were recorded. RESULTS: There were 62 male and 103 female patients. The mean age was 61.28±11.4 years, and the mean BMI was 26.23±4.49. A significant improvement (p<0.001) was observed in VAS, WOMAC, and OKS values of patients with K-L grade I-III osteoarthritis. Patients with K-L grade IV osteoarthritis showed no statistically significant improvement. No serious complications were observed in the patients. In addition, a statistically significant decrease was found in the daily doses of paracetamol/tramadol and in the number of patients who continued to use NSAIDs after 12 months of follow-up. CONCLUSION: The results of the study suggest that minimally manipulated autologous fat tissue injections are effective and safe treatment methods for patients with grade I-III knee osteoarthritis. The results may not be satisfactory in severe osteoarthritis due to the limited capabilities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".