Subtrochlear sclerosis evaluation for medial coronoid disease detection and dynamic distal ulnar ostectomy for reduction of disease progression in Labrador retriever puppies
Bibliographic record
Abstract
Objective To evaluate methods used in the early radiographic detection of subtrochlear ulna sclerosis (STS), which could successfully predict the development of medial coronoid disease (MCD) in four-month-old puppies, and at the same time perform distal dynamic ulnar ostectomy (DDUO) to prevent or reduce disease development. Study design Retrospective descriptive radiographic study. Sample population One hundred and fifty-two Labrador retriever puppies from the South African Guide Dog Association were available for the study. Materials and methods Extended mediolateral radiographs were evaluated for STS by means of a subjective (score 0-3), as well as semi-quantitative and objective grading scores, given as a percentage of an STS region of interest compared to normal ulna medulla. Evaluations were performed by a surgery resident, specialist veterinary radiologist (SVR) and specialist veterinary surgeon. Lameness and pain were also evaluated and were combined with the STS scores to give a total joint involvement score. DDUO was performed on all elbows with >20% joint involvement. At 12-months-old, all dogs underwent elbow computed tomography to determine the development of MCD. Results The only method predictive of disease development was SVR’s subjective evaluation of STS, with a sensitivity of 46% and specificity of 81%. Twenty-two dogs not receiving surgery still developed MCD. One of 21 dogs receiving DDUO surgery developed MCD. The surgery had significant protective effects, with surgical candidates being 13 times less likely to develop MCD (odds ratio = 13.3, P = 0.026). Conclusion Labrador retrievers and other known at-risk breeds should be screened at four months of age for STS through radiographic assessment by an SVR. If deemed at risk, DDUO surgery is advised to prevent or reduce MCD development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".