Autologous adipose-derived vascular stromal component injection offers a safe and effective method for treating knee osteoarthritis: A one-year double-blind, randomized controlled clinical trial
Bibliographic record
Abstract
Abstract Background: Currently, conservative treatment for knee osteoarthritis (KOA) has limited efficacy, and autologous adipose-derived stromal vascular fraction (SVF) knee injections as a novel treatment approach are receiving widespread attention. Our study aimed to explore the efficacy and safety of SVF treatment for KOA patients. Methods: This double-blind, randomized controlled trial recruited unilateral KOA patients from the Rehabilitation Departments of the First, Second, and Third Affiliated Hospitals of Gannan Medical University. Sixty-six unilateral KOA participants were randomly divided into three groups for conventional treatment, SVF treatment, and a combination of SVF and conventional treatment. We compared the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores, Visual Analogue Scale (VAS) scores, range of motion (ROM) of the knee, cartilage degeneration situation, and the incidence, nature, and severity of adverse events after treatment at 7 days, 1 month, 6 months, and 12 months. Results:A total of 62 patients completed the follow-up. There were no significant baseline differences among the groups. Our results demonstrated that, compared to baseline values, average VAS and WOMAC scores significantly decreased, while ROM significantly increased in the SVF and combination treatment groups during the 12-month follow-up, with a significant difference when compared to the control group (P< 0.05). Cartilage regeneration was observed in the combination treatment group at the 12-month follow-up (P < 0.05). No serious adverse events were observed during the 12-month follow-up, and no significant difference was noted in the incidence of complications among the three groups (P > 0.05). Conclusion:A single SVF injection demonstrates good safety, no serious adverse reactions, and can achieve better therapeutic effects when combined with conventional treatment, which is worth further investigation and promotion in clinical practice. Trial registration Chinese Clinical Trial Registry (ChiCTR2300074894). First trial registration in the format 18/08/2023
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".