Ankylosing spondylitis, chronic fatigue and depression improved after stromal vascular fraction treatment for osteoarthritis: a case report
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis is a prevalent chronic disease that impacts quality of life and imposes a heavy economic burden. Despite this there is no confirmed treatment that could prevent progressive destruction of osteoarthritic joints. Mesenchymal stem cells with their regenerative and immunosuppressive properties have emerged as a potential therapy. CASE PRESENTATION: This case study describes the impact of autologous adipose-derived stromal vascular fraction treatment on a 27-year-old Australian woman with osteoarthritis and multiple comorbidities of ankylosing spondylitis, chronic pain syndrome, and post-traumatic stress disorder as assessed by magnetic resonance imaging, Western Ontario and McMaster Universities Osteoarthritis Index, and Hip Disability and Osteoarthritis Outcome Score. Following standardized stromal vascular fraction treatment protocols for osteoarthritis of her hips and knee, the functional status of her hips was measured by Hip Disability and Osteoarthritis Outcome Score at 3 months, 6 months, and 3 years. CONCLUSIONS: Our patient showed dramatic improvements to her quality of life and symptoms of osteoarthritis were reduced. Interestingly, along with improvements in her knee and hips her other comorbidities such as ankylosing spondylitis, depression, anxiety, and fatigue exhibited marked improvement. She ceased the use of a wheelchair and walking support and, with increased mobility, had gained independence. These findings are suggestive of the therapeutic effects of stromal vascular fraction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| 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".