Evaluation of subchondral bone cysts in canine elbows with radiographic osteoarthritis secondary to elbow dysplasia
Bibliographic record
Abstract
OBJECTIVE: To investigate whether subchondral bone cysts (SBCs) were present in dogs with radiographic elbow osteoarthritis (OA) and to investigate their relationship with radiographic OA severity. STUDY DESIGN: Retrospective cross-sectional study. SAMPLE POPULATION: Thirty-eight Labrador retrievers (total of 76 elbows). METHODS: Elbow computed tomography (CT) images of 18 young (≤2 years old) and 20 old (>2 years old) Labrador retrievers, which presented for elbow-associated lameness, were reviewed. Radiographic elbow OA was graded into four groups based on the largest osteophyte size on CT. The presence, number, and maximum diameter of SBCs were determined. RESULTS: Subchondral bone cysts were only identified in elbows with osteophytic new bone formation. The number and size of SBCs were associated with radiographic OA severity (p < .001 and p = .041 respectively). Specifically, the rate at which SBCs were present increased for both moderate and severe OA in comparison with the mild OA (moderate OA RR = 2.46, 95% CI 2.08-2.92, p < .001; severe OA RR = 5.60, 95% CI 4.79-6.55, p < .001). For dogs with severe OA, there was an increased likelihood that their SBCs were larger than SBCs from dogs with mild OA (OR = 1.056, 95% CI 1.012-1.101, p = .012). No SBCs were observed in elbows without radiographic evidence of OA. CONCLUSION: Subchondral bone cysts were identified as a feature of radiographic elbow osteoarthritis in Labrador retrievers, and their number and size were indicative of the presence and severity of radiographic elbow OA. CLINICAL SIGNIFICANCE: Subchondral bone cysts are a potential imaging biomarker for quantitative assessment for canine OA.
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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 | 0.003 |
| 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 | 0.001 |
| 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".