Osteophytosis, Subchondral Bone Sclerosis, Joint Effusion and Soft Tissue Thickening in Canine Experimental Stifle Osteoarthritis: Comparison Between 1.5 T Magnetic Resonance Imaging and Computed Radiography
Bibliographic record
Abstract
OBJECTIVE: To compare use of 1.5 T magnetic resonance imaging (MRI) and computed radiography (CR) for morphologic and temporal evaluation of osteophytosis, subchondral sclerosis, joint effusion, and synovial thickening in experimentally induced canine stifle osteoarthritis (OA). STUDY DESIGN: Prospective study. ANIMALS: Dogs (n=8). METHODS: CR (mediolateral and caudocranial projections) and MRI (dorsal 3D T1-weighted gradient echo, sagittal 3D SPGR and T2-weighted fast spin echo with fat saturation) were performed at baseline (n=8) and at week 4 (n=5), week 8 (n=8), and week 26 (n=5) after cranial cruciate ligament transection. Osteophytosis, subchondral bone sclerosis, and joint effusion were scored on CR and MRI, and synovial thickening on MRI. RESULTS: MRI was more sensitive than CR for detection of osteophytosis and could better discriminate joint effusion from soft tissue thickening, although scores for these variables strongly correlated between modalities (rho=0.94 [osteophytosis] and 0.80 [effusion]; P<.001). Scores for subchondral bone sclerosis also correlated (rho=0.54, P<.004), although this variable may have been over interpreted on CR. Joint effusion and synovial thickening peaked at week 8, before partially regressing at week 26. Conversely, osteophytosis and sclerosis progressed semi-linearly over 26 weeks. CONCLUSION: MRI is more sensitive than radiography in assessing onset and progression of osteophytosis in canine experimental stifle OA and provides enhanced discrimination between joint effusion and synovial thickening. CLINICAL RELEVANCE: MRI is as a more powerful imaging modality that should be increasingly used in animals to assess the joint related effects of disease-modifying OA drugs.
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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 | 0.001 |
| 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 | 0.001 | 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".