Epidemiology and clinical management of elbow joint disease in dogs under primary veterinary care in the UK
Bibliographic record
Abstract
Abstract Background Conditions affecting the elbow joint are a common cause of lameness in dogs. Primary-care veterinary clinical data are now recognised as a valuable research resource. Using data from the VetCompass Programme, this study aimed to report the frequency and risk factors for elbow joint disease in dogs under primary veterinary care in the UK and describe clinical management. Results From 455,069 dogs under veterinary care, the one-year period prevalence for elbow joint disease diagnosis was 0.56% (95% CI: 0.53–0.60). Of 616 incident cases, the most common specific variants of elbow joint disease were osteoarthritis (n = 468, 75.97%), elbow dysplasia (190, 30.84%) and traumatic (41, 6.66%). The most common signs described by the owners were lameness (n = 466, 75.65%), difficulty exercising (123, 19.97%) and pain (86, 13.96%). The most common findings recorded on veterinary examination were pain (n = 283, 45.94%), lameness (278, 45.13%) and reduced range of movement (243, 39.45%). Common medications used included non-steroidal anti-inflammatory drugs (n = 544, 88.31%), tramadol (121, 19.64%) and disease modifying agents (118, 19.16%). Of 109 deaths involving euthanasia with information available from the 616 incident cases, elbow joint disease contributed to the decision to euthanase in 45 (41.28%) dogs. Five breeds showed increased odds of elbow joint disease compared with crossbred dogs: Rottweiler (OR: 6.16, 95% CI 3.89–9.75), Labrador Retriever (OR: 5.94, 95% CI 4.65–7.60), German Shepherd Dog (OR: 4.13, 95% CI 2.88–5.93), Golden Retriever (OR: 3.11, 95% CI 1.93–5.00) and English Springer Spaniel (OR: 2.00, 95% CI 1.26–3.18). Additional risk factors included having an adult bodyweight that was equal or higher than their breed/sex mean, advancing age, being male, being neutered, being insured and larger bodyweight. Conclusions Elbow joint disease is a relatively common diagnosis in dogs and has a high welfare impact as evidenced by the high proportion of cases recorded with pain, lameness and analgesic therapy. There are strong breed predispositions, in particular for large breed dogs. These findings present a clear case for improved breeding programmes to reduce the burden of elbow joint disease.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".