Reliability of radiological assessment of ulnar trochlear notch sclerosis in dysplastic canine elbows
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to quantify the sensitivity and specificity of visual assessment of radiographs of the canine elbow in detecting ulnar trochlear notch sclerosis, to establish interobserver and intra-observer variation for the presence and grade of sclerosis and to quantify the effect of radiographic exposure on observer grading. METHODS: Mediolateral elbow radiographs were obtained from Labrador retrievers (n=34) aged between six and 18 months. Radiographs from dogs with an arthroscopic diagnosis of fragmented medial coronoid process (n=17) and those from a control population (n=17) were subjected to observer grading for the presence or absence of and the grade of ulnar trochlear notch sclerosis. Interobserver and intra-observer variation and observer sensitivity and specificity were calculated. Digital data from the ulnar trochlear notch were correlated with mean observer grade to quantify the effect of radiographic exposure on observer grade. RESULTS: Interobserver agreement was "fair" (kappa=0.251 to 0.369) and intra-observer agreement was "moderate" to "substantial" (kappa=0.462 to 0.667). The sensitivity of observer assessment was 72 per cent with a specificity of 22 per cent. Mean observer grade was not significantly correlated with the degree of radiographic exposure (P=0.70). CLINICAL SIGNIFICANCE: Ulnar trochlear notch sclerosis is a phenomenon associated with fragmented medial coronoid process. However, interobserver agreement in grading this feature is only fair, being identified by observers with moderate sensitivity but with relatively poor specificity. This low specificity may predispose to overdiagnosis in clinical cases. Intra-observer agreement is moderate to substantial, suggesting that individuals can reliably quantify this radiological feature on multiple occasions. The ability of observers to assess the degree of sclerotic change is not significantly affected by radiographic exposure.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".