Radiographic, Computed Tomographic, and Arthroscopic Evaluation of Experimental Radio‐Ulnar Incongruence in the Dog
Bibliographic record
Abstract
OBJECTIVE: To compare the diagnostic value of arthroscopy, computed tomography (CT), and radiography for evaluation of radio-ulnar incongruence (RUI). STUDY DESIGN: Experimental evaluation of induced progressive RUI. SAMPLE POPULATION: Cadaveric Labrador forelimbs (n=11). METHODS: The radius was shortened by 1, 2, and 3 mm with a surgical model of RUI. RUI was scored on radiographs, CT (2 radiologists), and arthroscopy (2 surgeons) before and after each modification. The sensitivity and specificity of each modality were compared. The effects of arthroscope and elbow position on arthroscopy observations were evaluated. Agreement between surgeons, radiologists, and each imaging technique and the known status of the elbow was calculated. RESULTS: Complete arthroscopic sessions had an averaged sensitivity of 94% and specificity of 81.9%. The ability to detect mild incongruity (1 mm step) was greater at the incisure than other locations (P<.001). The average sensitivity and specificity of radiography were 99.3% and 42.4%, and for CT were 85.05% and 45.8%, respectively. The average agreement between imaging techniques and the known status of the elbows was greater with complete arthroscopic sessions (89.75%) than radiography (70.1%) and CT (76.85%). Inter-investigator agreement was greater between surgeons scoring arthroscopic examinations (88.6%) than radiologists scoring CT studies (43.9%). CONCLUSIONS: Evaluation of arthroscopic images allows sensitive and reproducible detection of experimental RUI, especially at the incisure. Arthroscopic evaluation of experimental RUI reached a higher diagnostic value than radiographs and CT images, because of its specificity and reproducibility. CLINICAL RELEVANCE: The diagnostic value and reproducibility of arthroscopy may compare favorably with those of CT when evaluating RUI in dogs with elbow disease.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".