Evaluation of avocado and soybean unsaponifiable extracts for treatment of horses with experimentally induced osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the use of a combination of avocado and soybean unsaponifiable (ASU) extracts for the treatment of experimentally induced osteoarthritis in horses. ANIMALS: 16 horses. PROCEDURES: Osteoarthritis was induced via osteochondral fragmentation in 1 middle carpal joint of each horse; the other joint underwent a sham operation. Horses were randomly allocated to receive oral treatment with ASU extracts (1:2 [avocado-to-soybean] ratio mixed in 6 mL of molasses; n = 8) or molasses (6 mL) alone (placebo treatment; 8) once daily from days 0 to 70. Lameness, response to joint flexion, synovial effusion, gross and histologic joint assessments, and serum and synovial fluid biochemical data were compared between treatment groups to identify effects of treatment. RESULTS: Osteochondral fragmentation induced significant increases in various variables indicative of joint pain and disease. Treatment with ASU extracts did not have an effect on signs of pain or lameness; however, there was a significant reduction in severity of articular cartilage erosion and synovial hemorrhage (assessed grossly) and significant increase in articular cartilage glycosaminoglycan synthesis, compared with placebo-treated horses. CONCLUSIONS AND CLINICAL RELEVANCE: Although treatment with ASU extracts did not decrease clinical signs of pain in horses with experimentally induced osteoarthritis, there did appear to be a disease-modifying effect of treatment, compared with findings in placebo-treated horses. These objective data support the use of ASU extracts as a disease-modifying treatment for management of osteoarthritis in horses.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".