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Record W4409889809 · doi:10.3389/fvets.2025.1565922

A proposed framework for practical multimodal management of osteoarthritis in growing dogs

2025· review· en· W4409889809 on OpenAlexaff
Denis J. Marcellin‐Little, Donald A. Hulse, Janice Huntingford, Tamara Grubb, Matthew W. Brunke, Arielle Pechette Markley

Bibliographic record

VenueFrontiers in Veterinary Science · 2025
Typereview
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsVétoquinol (Canada)
Fundersnot available
KeywordsOsteoarthritisMedicineCruciate ligamentOrthopedic surgeryPhysical therapyRehabilitationHip dysplasiaElbowDysplasiaPhysical medicine and rehabilitationAnterior cruciate ligamentSurgeryPathologyRadiographyAlternative medicine

Abstract

fetched live from OpenAlex

Osteoarthritis (OA) is a ubiquitous problem affecting dog joints, particularly the hip, elbow, stifle, and spine. OA most often results from developmental orthopedic problems such as hip dysplasia, elbow dysplasia, and patellar luxation and from injuries to the cranial cruciate ligament. Several management approaches have been proposed to manage OA, including steps to modulate growth, physical activity, and exercise, nutrition and nutritional supplementation, medications, physical rehabilitation, and surgical procedures. This article is the first in a series of articles that propose steps for practical OA management in dogs at various life stages. The review presented here focuses on growing dogs. The text describes the early pathophysiology and diagnosis of OA. The physical, nutritional, analgesic, and surgical management options of OA in growing dogs are presented. The application of these management options is described for three dogs. The overall approach to the management of OA in growing dogs is discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.951
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.088
GPT teacher head0.419
Teacher spread0.331 · 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 teacher head, not a consensus.

Study designOther design
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

Citations6
Published2025
Admission routes1
Has abstractyes

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