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Record W2603547887 · doi:10.4314/ovj.v7i1.6

Glucosamine and chondroitin use in canines for osteoarthritis: A review

2017· review· en· W2603547887 on OpenAlexaff
Angel Bhathal, Meredith Spryszak, Christopher Louizos, Grace Frankel

Bibliographic record

VenueOpen Veterinary Journal · 2017
Typereview
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsUniversity of ManitobaUniversity of Alberta
Fundersnot available
KeywordsOsteoarthritisGlucosamineChondroitin sulfateChondroitinMedicinePhysical therapyQuality of life (healthcare)Alternative medicinePathologyBiologyGlycosaminoglycan

Abstract

fetched live from OpenAlex

Osteoarthritis is a slowly progressive and debilitating disease that affects canines of all breeds. Pain and decreased mobility resulting from osteoarthritis often have a negative impact on the affected canine's quality of life, level of comfort, daily functioning, activity, behaviour, and client-pet companionship. Despite limited and conflicting evidence, the natural products glucosamine hydrochloride (HCl) and chondroitin sulfate are commonly recommended by veterinarians for treating osteoarthritis in dogs. There is a paucity of well-designed clinical veterinary studies investigating the true treatment effect of glucosamine and chondroitin. The purposes of this review article are to provide a brief background on glucosamine and chondroitin use in canine osteoarthritis and to critically review the available literature on the role of these products for improving clinical outcomes. Based on critical review, recommendations for practice are suggested and a future study design is proposed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.529
GPT teacher head0.512
Teacher spread0.018 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations55
Published2017
Admission routes1
Has abstractyes

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