Measurement of Ulnar Subtrochlear Sclerosis Using a Percentage Scale in Labrador Retrievers with Minimal Radiographic Signs of Periarticular Osteophytosis
Bibliographic record
Abstract
OBJECTIVE: To report the development of a measurement method for quantifying ulnar subtrochlear sclerosis (STS) in Labrador Retrievers. STUDY DESIGN: Prospective blinded study. SAMPLE POPULATION: Radiographs of Labrador Retrievers elbows (n=30) with minimal radiographic signs of periarticular osteophytosis. METHOD: Measurement of STS as a % of the distance between 2 standardized radiographic landmarks (%STS) was developed. Mediolateral radiographic projections of flexed elbows were collected from 2 cohorts termed diseased (n=15; confirmed disease of the medial coronoid process) and control (n=15; free from clinically evident disease). Five observers blindly assessed each radiograph for radiographic technique, elbow positioning, periarticular osteophytosis, and STS, which, if present, was measured and assigned a %STS score. Intraobserver and interobserver variations in measuring STS and the ability to differentiate study cohorts were assessed using receiver operator curve (ROC) characteristics. A P-value of <.05 was considered significant. RESULTS: Median %STS for diseased elbows was 47% (range, 0-74%) and 0% (range, 0-62%) for control elbows. Correlations were not significantly different between each observer's assessments of %STS, with a median Spearman's P-value of .75 (range, .67-.86). All observers differentiated the 2 cohorts with "fair-good" accuracy, with a median ROC value of 0.81 (range, 0.75-0.88). CONCLUSION: Measurement of %STS in Labrador Retrievers was repeatable for each observer and repeatable between observers. CLINICAL RELEVANCE: A method for measuring STS allows comparison of Labrador Retrievers of different sizes, is easy to perform, and could be used to investigate the clinical significance of STS in this breed.
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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 | 0.004 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".