COMPARISON OF MAGNETIC RESONANCE IMAGING, COMPUTED TOMOGRAPHY, AND RADIOGRAPHY FOR ASSESSMENT OF NONCARTILAGINOUS CHANGES IN EQUINE METACARPOPHALANGEAL OSTEOARTHRITIS
Bibliographic record
Abstract
We compared the ability of 1.5 T magnetic resonance imaging (MRI), computed tomography (CT), and computed radiography (CR) to evaluate noncartilaginous structures of the equine metacarpophalangeal joint (MCP), and the association of imaging changes with gross cartilage damage in the context of osteoarthritis. Four CR projections, helical single-slice CT, and MRI (Ti-weighted gradient recalled echo [GRE], T2*-weighted GRE with fast imaging employing steady-state acquisition [FIESTA], T2-weighted fast spin echo with fat saturation, and spoiled gradient recalled echo with fat saturation ISPGR-FS]) were performed on 20 racehorse cadaver forelimbs. Osteophytosis, synovial effusion, subchondral bone lysis and sclerosis, supracondylar lysis, joint fragments, bone marrow lesions, and collateral desmopathy were assessed with each modality. Interexaminer agreement was inferior to intraexaminer agreement and was generally moderate (i.e., 0.4 < kappa < 0.6). Subchondral bone sclerosis scores using CT or MRI were correlated significantly with the reference quantitative CT technique used to assess bone mineral density (P < 0.0001). Scores for subchondral lysis and osteophytosis were higher with MRI or CT vs. CR (P < 0.0001). Although differences between modalities were noted, osteophytosis, subchondral sclerosis, and lysis as well as synovial effusion were all associated with the degree of cartilage damage and should be further evaluated as potential criteria to be included in a whole-organ scoring system. This study highlights the capacity of MRI to evaluate noncartilaginous changes in the osteoarthritic equine MCP joint.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".