Efficacy of an oral hyaluronate and collagen supplement as a preventive treatment of elbow dysplasia
Bibliographic record
Abstract
One hundred and five Labrador dogs were randomly divided into two groups to determine the number of animals that develop elbow dysplasia when treated with an oral supplement compared to untreated ones. Efficacy of the oral treatment was also evaluated once illness was diagnosed. The supplement (Hyaloral) contained hyaluronic acid, hydrolysed collagen, glucosamine, chondroitin sulphate, and gamma oryzanol. Clinical evaluation of the elbow joints was completed at months 3, 6, 12, and 20 by orthopaedic evaluations, radiography, serologic and blood analysis, and veterinarian evaluation of dysplasia symptoms. All side effects were recorded. In the control group, 33.3% of the dogs developed radiographic evidence of elbow dysplasia compared to 18.5% in the treated group. Symptoms of dysplasia at 12 months differed between the treated (12.5%) and control (61.5%) animals, and were significantly different at 20 months (p < 0.05). Differences in lameness along with movement and swelling of the elbows between groups were observed after 12 months. The treated group had improved significantly by the last visit (p < 0.05). No adverse side effects were reported. In conclusion, oral treatment with Hyaloral may have a potential cumulative action that provides protection against dysplasia and significantly improves symptoms of elbow dysplasia.
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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) | 0.000 | 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 | 0.000 | 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".