Radiographic, computed tomographic, and arthroscopic diagnosis of radioulnar incongruence in dogs with medial coronoid disease
Bibliographic record
Abstract
OBJECTIVE: To characterize radioulnar incongruence (RUI) in large dogs with medial coronoid disease (MCD) and determine the agreement between radiography, computed tomography (CT), and arthroscopy. STUDY DESIGN: Prospective observational case series. ANIMALS: Twenty-four large-breed dogs under 3 years of age with confirmed MCD. METHODS: MCD and incongruence were evaluated by radiographs and CT. RUI was measured along the ulnar commissure, mid-body, and apex of the medial coronoid process (MCP) by CT and arthroscopy. Joints with RUI ≥ 2 mm were considered incongruent. Variables recorded during arthroscopic treatment included type of MCD, RUI, and modified Outerbridge score. Agreement between the 3 diagnostic imaging techniques was evaluated, and the association between RUI and cartilage damage was analyzed. RESULTS: Single fragmentation was the most common MCD according to CT and arthroscopy. RUI was associated with degenerative joint disease according to CT and with cartilage damage according to arthroscopy. RUI was diagnosed in 26% of elbows by radiography, in 35% of elbows by CT, in 78% of elbows according to CT measurements of RUI ≥ 2 mm, and in 57% of joints by arthroscopy. RUI was most commonly detected at the ulnar commissure with CT and at the apex of the MCP with arthroscopy. Imaging modalities were in poor to fair agreement with arthroscopy when evaluating RUI. CONCLUSION: CT and arthroscopy provided similar detection of MCD and cartilage disease but disagreed in the assessment and localization of RUI. CLINICAL SIGNIFICANCE: CT does not reliably predict arthroscopic assessment of RUI and associated treatment decisions for MCD.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".