Computed tomography findings in a cohort of 169 dogs with elbow dysplasia - a retrospective study
Bibliographic record
Abstract
BACKGROUND: Canine elbow dysplasia (CED) is a complex developmental skeletal disorder associated with a number of pathological conditions within the cubital joint. Because CED is a heritable disease, it is important to identify and remove the affected animals from breeding. The first objective of this study was to describe the prevalence of medial coronoid process disease (MCPD) without (MCD) or with (FMCP) fragmented medial coronoid process, osteochondrosis (OC) and/or osteochondritis dissecans (OCD), ununited anconeal process (UAP), radio-ulnar incongruence (INC R-U) and humero-ulnar incongruence (INC H-U) in dogs with the use of CT imaging. The second aim was to determine the influence of demographics on the prevalence of investigated pathologies in dogs with clinical evidence of elbow dysplasia. RESULTS: In this retrospective study, CT data records of 169 dogs of different breeds presented to the small animal veterinary clinic from 2012 to 2018 were included. 69.23% of dogs diagnosed with CED were young (≤ 2 years old). The mean age of dogs presented with INC R-U was 1.68 ± 1.82 years, while in dogs without INC R-U the mean age was 2.64 ± 2.59 years. The mean age of dogs with INC H-U was 1.94 ± 2.06 years, while without INC H-U 3.29 ± 2.09 years. Labrador Retrievers, German Shepherd and Bernese Mountain dogs were most frequently presented with CED-associated lameness. In 122 dogs OA of varying severity was found. CONCLUSION: INC H-U, FMCP and MCD were among the most frequently found components of CED found in the present study. OCD and UAP were the least frequently diagnosed. Dogs presented with INC R-U and INC H-U were significantly younger than dogs without these CED components. Boxers, Dog de Bordeaux, American Staffordshire terriers and mixed-breed dogs were diagnosed later in life than the other breeds. OA of varying severity was found in 72.18% of dogs. Males accounted for more than 75% of the study population.
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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 | 0.001 |
| 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 | 0.000 |
| 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".