Adipose Derived Stromal Cell (ADSC) Injections for Pain Management of Osteoarthritis in the Human Knee Joint
Bibliographic record
Abstract
BACKGROUND: This safety and feasibility study used autologous adipose-derived stromal vascular cells (the stromal vascular fraction [SVF] of adipose tissue), to treat 8 osteoarthritic knees in 6 patients of grade I to III (K-L scale) with initial pain of 4 or greater on a 10-point Visual Analog Scale (VAS). OBJECTIVES: The primary objective of the study was evaluation of the safety of intra-articular injection of SVF. The secondary objective was to assess initial feasibility for reduction of pain in osteoarthritic knees. METHODS: Adipose-derived SVF cells were obtained through enzymatic disaggregation of lipoaspirate, resuspension in 3 mL of Lactated Ringer's Solution, and injection directly into the intra-articular space of the knee, with a mean of 14.1 million viable, nucleated SVF cells per knee. Metrics included monitoring of adverse events and preoperative to postoperative changes in the Western Ontario and McMaster Universities Arthritis Index (WOMAC), the VAS pain scale, range of motion (ROM), timed up-and-go (TUG), and MRI. RESULTS: No infections, acute pain flares, or other adverse events were reported. At 3-months postoperative, there was a statistically significant improvement in WOMAC and VAS scores (P < .02 and P < .001, respectively), which was maintained at 1 year. Physical therapy measurements for ROM and TUG both improved from preoperative to 3-months postoperative. Standard MRI assessment from preoperative to 3-months postoperative showed no detectable structural differences. All patients attained full activity with decreased knee pain. CONCLUSIONS: Autologous SVF was shown to be safe and to present a new potential therapy for reduction of pain for osteoarthritis of the knee. LEVEL OF EVIDENCE 4: Therapeutic.
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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 | 0.000 |
| 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 | 0.001 |
| 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".