Radiographic, Computed Tomographic, and Arthroscopic Findings in Labrador Retrievers With Medial Coronoid Disease
Bibliographic record
Abstract
OBJECTIVE: To describe the radiographic, computed tomographic (CT), and arthroscopic findings in different age groups of Labrador Retrievers diagnosed with medial coronoid disease (MCD), and to compare the ulnar subtrochlear sclerosis (STS) observed on radiographs with the ratio between the mean attenuation of the ulnar subtrochlear bone and the mean attenuation of the cortical bone measured on CT. STUDY DESIGN: Prospective clinical study. ANIMALS: Dogs (n = 31; 31 elbow joints) and 6 healthy Labrador Retrievers (6 elbow joints). METHODS: Radiographic, CT, and intraoperative arthroscopic images (2008-2012) were evaluated. Statistical analysis was performed for the descriptive study to evaluate the difference in findings between age groups and to investigate the correlation between radiographic and CT evaluated ulnar STS. RESULTS: Ulnar STS (87.6%) was the most common radiographic findings in dogs ≤12 months and blurring of the cranial edge of the medial coronoid process (MCP; 66.7%) was the most common radiographic findings in dogs >12 months. MCP fragmentation was the most common CT finding in both age groups (93.8% [≤12 months]; 66.7% [>12 months]). A displaced fragment (68.8%) was the most common arthroscopic finding in dogs ≤12 months whereas osteochondromalacia (53.3%) was the most common finding in dogs >12 months. Sensitivity of radiography in detecting MCD was 93.8% (≤12 months) and 73.3% (>12 months) and for CT was 93.8% (≤12 months) and 66.7% (>12 months). Radiographic evaluated ulnar STS was strongly correlated with CT evaluated ulnar STS. CONCLUSION: Wide ranges of radiographic, CT, and arthroscopic findings in Labrador Retrievers diagnosed with medial coronoid disease were identified.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".