Quantitative Radiographic Evaluation of Elbow Incongruity in Labrador and Golden Retrievers with Confirmed Medial Coronoid Disease
Bibliographic record
Abstract
Objectives The purpose of this study was to objectively estimate humeroradial (HR), humeroulnar (HU) and radioulnar (RU) congruity on mediolateral elbow radiographs of Labrador and Golden Retrievers without and with medial coronoid disease (MCD), and to determine the correlation between traditional and modified RU-step assessment techniques. Materials and Methods Extended mediolateral elbow radiographs of Labrador and Golden Retrievers without MCD (control group) and with confirmed MCD (diseased group) were investigated. Absolute and average HR and HU distances were determined and standardized by the radius of the corresponding humeral condyle. Traditional RU-step was measured, and a modified procedure of RU-step assessment was generated. The correlation between the two RU-step assessment procedures was tested. Results A total of 131 (197 elbows) Labrador and Golden Retrievers met the criteria for inclusion in the control and diseased groups. The normalized HR and HU distances increased significantly (p ≤ 0.002) in dogs with MCD. There was a significant increase (p < 0.0001) in the traditional and modified RU-step in dogs with MCD. A significant correlation (r s = 0.74, p < 0.0001) was identified between traditional and modified RU-step calculated for control and diseased elbows. Clinical Relevance Dogs with confirmed MCD had quantitative radiographic evidences of elbow incongruity. Modified RU-step procedure may be an alternative to the traditional technique and can be utilized during routine quantification of HR and HU congruity. Validation of the reported measurements is, however, warranted.
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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 | 0.002 | 0.000 |
| 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) | 0.001 | 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".