Radiographic and computed tomographic findings in canine elbow dysplasia: A retrospective research
Bibliographic record
Abstract
Long before the first radiograph of a lame dog was taken, breeders of large and giant breeds recognised that some puppies developed a persistent forelimb limp that worsened with exercise and never fully resolved. We now know this constellation of lesions as elbow dysplasia (ED), and advanced imaging has transformed how we identify its subtypes. This retrospective research reviewed the medical records and imaging archives of 181 dogs (272 affected elbows) diagnosed with ED at the Bangkok Academy of Veterinary Medicine between January 2019 and December 2023. Each elbow had been evaluated by standard radiography (mediolateral flexed and craniocaudal views) and multi-detector computed tomography (CT; 0.625 mm slice thickness). Two board-certified veterinary radiologists independently graded every elbow using the International Elbow Working Group (IEWG) protocol and classified lesions into fragmented coronoid process (FCP), ununited anconeal process (UAP), osteochondritis dissecans (OCD), elbow incongruity, and combined pathology. FCP was the most common lesion (34.2% of affected elbows), followed by OCD (22.1%), incongruity (18.8%), UAP (18.3%), and combined pathology (6.6%). Dogs aged 1-2 years constituted the largest diagnostic cohort (37.5%), with Labrador Retrievers, German Shepherds, and Golden Retrievers together accounting for 57.4% of cases. CT detected 94.7% of FCP lesions confirmed at arthroscopy or surgery, whereas radiography identified only 61.8% (p<0.001). The CT advantage was greatest for incongruity (88.6% vs. 54.3%) and combined lesions (82.4% vs. 47.1%). Radiographic sensitivity was adequate only for UAP (82.4%), where the displaced fragment is typically large and well-mineralised. IEWG Grade II (moderate arthrosis, 2-5 mm osteophytes) was the most frequent grading at presentation (49.3%), followed by Grade III (severe, >5 mm; 27.2%) and Grade I (mild, <2 mm; 23.5%). Inter-observer agreement for CT-based subtype classification was excellent (?=0.91) compared with moderate agreement for radiography alone (?=0.68). These findings confirm that CT is the superior diagnostic tool for elbow dysplasia subtyping in a tropical referral population and should be considered the default imaging modality whenever surgical planning is anticipated. Radiography retains a screening role but underestimates pathology burden, particularly for FCP and incongruity, the two subtypes most likely to benefit from early arthroscopic intervention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".