Predicting Long-Term Benefits of Micro-Fragmented Adipose Tissue Therapy in Knee Osteoarthritis: Three-Year Follow-Up on Pain Relief and Mobility
Bibliographic record
Abstract
Objectives: This study aims to assess the clinical efficacy of micro-fragmented adipose tissue (MFAT) therapy over three years in patients with KOA and to determine whether short-term improvements at three months can forecast long-term outcomes. Methods: A retrospective, observational study was conducted on 335 patients diagnosed with KOA who received a single MFAT injection. The patients were followed up at 3 months, 6 months, 1 year, 2 years, and 3 years, with assessments using the Visual Analog Scale (VAS), Oxford Knee Score (OKS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and Knee Injury and Osteoarthritis Outcome Score (KOOS). Statistical analysis was performed to assess the differences in preoperative and postoperative scores (VAS, OKS, WOMAC, KOOS) to evaluate the predictive role of 3-month score changes on long-term clinical outcomes. Results: All measured scores (VAS, OKS, WOMAC, KOOS) showed significant improvement at 3 months, with sustained improvements through 3 years (p < 0.001). Early score changes at 3 months were significantly associated with improved clinical outcomes at 1, 2, and 3 years (p < 0.05). Logistic regression confirmed early post-treatment improvements as independent predictors of long-term benefit, except for the VAS score at 3 years (p = 0.098). A comparative analysis between completers and dropouts showed no baseline differences; however, significant outcome differences emerged at later follow-up points. Due to insufficient data at the 3-year mark among dropouts, statistical comparisons were not possible for that time point. Conclusions: MFAT treatment was associated with consistent symptomatic improvement in patients with KOA, and early clinical response at 3 months served as a reliable predictor of long-term pain and function outcomes. While this study focused on patient-reported symptom relief and not structural regeneration, the results support MFAT as a minimally invasive option for symptom management. Early post-treatment response may serve as a useful tool for clinicians to predict long-term therapeutic success and personalize treatment strategies for KOA patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".