A Prospective, Randomized, Masked, and Placebo-Controlled Efficacy Study of Intraarticular Allogeneic Adipose Stem Cells for the Treatment of Osteoarthritis in Dogs
Bibliographic record
Abstract
Osteoarthritis (OA) is a degenerative joint disease with a high prevalence in dogs. Mesenchymal stem cells have been used to treat humans, dogs, and horses with OA. This report describes a prospective, randomized, blinded, and placebo-controlled clinical efficacy study of intraarticular allogeneic adipose stem cells for the treatment of dogs with osteoarthritis. Health assessments and measurements of pain and activity impairment were performed at baseline and at selected time points through day 60. The primary outcome variable was the owner Client-Specific Outcome Measurement (CSOM) and secondary measures included veterinary pain on manipulation, veterinary global score, and owner global score. The dogs were treated with either a saline placebo or a single dose of allogeneic adipose-derived mesenchymal stem cells in either one or two joints. Seventy-four dogs were statistically analyzed for efficacy outcomes. Success in the primary outcome variable, CSOM, was statistically improved in the treated dogs compared to the placebo dogs (79.2% versus 55.4%, p=0.029). The veterinary pain on manipulation score (92.8% versus 50.2%, p=0.017) and the veterinary global score (86.9% versus 30.8%, p= 0.009) were both statistically improved in treated dogs compared to placebo. There was no detected significant difference between treated and placebo dogs in the incidence of adverse events or negative health findings. Allogeneic adipose-derived stem cell treatment was shown to be efficacious compared to placebo. This large study of dogs also provides valuable animal clinical safety and efficacy outcome data to our colleagues developing human stem cell therapy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".