Micro‐fragmented adipose tissue for the treatment of hip osteoarthritis: A prospective pilot study at 1‐year follow‐up
Bibliographic record
Abstract
Purpose: Micro-fragmented adipose tissue (MFAT) has been proposed as a promising option for hip osteoarthritis (OA). The aim of this prospective study was to evaluate clinical outcomes of MFAT injections in patients with hip OA. Methods: Thirty patients (19 men and 11 women, 55.7 ± 8.2 years) with symptomatic hip OA (Tönnis Grade 1-2) were treated with a single ultrasound-guided MFAT injection. Patients were evaluated at baseline and 1-3-6-12 months of follow-up with the Visual Analogue Scale (VAS), Western Ontario and McMaster Universities Arthritis Index (WOMAC), and Harris Hip Score (HHS). Adverse events were also documented. MFAT samples were evaluated for cell analysis and characterization. Results: = 0.012), while VAS and HHS scores showed a significant improvement from baseline to all follow-ups. The minimal clinically important difference (MCID) for the total WOMAC score was achieved in 56.7% of patients at 6 and 12 months. Better clinical improvement and MCID achievement were observed in mild compared to moderate hip OA. A total of 300.000 cells derived from MFAT produced small colonies from Day 10 (89.9 ± 61.2), increasing by Day 20 (129.9 ± 47.9). Conclusions: A single ultrasound-guided intra-articular MFAT injection represents a safe and promising option for hip OA treatment. However, the clinical benefit was partial, and better outcomes were observed in mild compared to moderate hip OA. Level of Evidence: Level IV.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".