Low quality of evidence for glucosamine‐based nutraceuticals in equine joint disease: Review of <i>in vivo</i> studies
Bibliographic record
Abstract
Nutraceuticals are increasingly applied to the management of equine arthritis and joint disease, particularly those based upon glucosamine and chondroitin sulphate. While the first report of using glucosamine in horses appeared more than 25 years ago, it was not until 1992 that isolated studies began to be reported. Since that time, 15 in vivo papers have been published in the equine literature, usually on products already commercially available and often seeking evidence for efficacy. These studies demonstrate an encouraging trend to manufacturers of these products investing in research, but most do not meet a quality standard that provides sufficient confidence in the results reported. This review discusses the entirety of published in vivo research on glucosamine-based nutraceuticals (GBN) for horses, including that on Cosequin, Cortaflex, Synequin, Sasha's EQ, Myristol, chondroitin sulphate, glucosamine sulphate and glucosamine hydrochloride; and considers experimental limitations of this research along with their impact on interpretation of results. A quality score was calculated for each paper according to preset quality criteria. A minimum quality standard of 60% was set as the threshold for confidence in interpretation of results. Of the 15 papers reviewed, only 3 met the minimum quality standard. Experimental limitations of each research paper are discussed. It is concluded that the quality of studies in this area is generally low, prohibiting meaningful interpretation of the reported results. New high quality research on GBN for horses is needed and recommendations for future research are discussed.
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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 | 0.039 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 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".