Development of an Ultrasound Imaging Atlas for Grading Osteoarthritis in the First Metatarsophalangeal Joint
Bibliographic record
Abstract
OBJECTIVE: Ultrasound (US) imaging may play a fundamental role in the earlier detection and assessment of first metatarsophalangeal joint (MTPJ) osteoarthritis (OA) because of its ability to depict tissue-specific morphologic changes before the point of irreversible structural damage. However, the role of US in supporting the diagnosis of OA in foot joints has not been clearly defined. The aims of this study were to develop a semiquantitative US atlas (the AUT ultrasound imaging [AUTUSI] atlas) to grade the degree of osteoarthritic change in the first MTPJ and to evaluate the intraexaminer and interexaminer reproducibility of using the atlas. METHODS: US images were obtained from 57 participants (30 participants with radiographically confirmed first MTPJ OA). The AUTUSI atlas supports the examination of grading joint effusion, synovial hypertrophy, synovitis, osteophytes, joint space narrowing, and cartilage thickness. Six examiners used the atlas to independently grade 24 US images across 2 sessions. Intraexaminer and interexaminer reproducibility were determined using percentage agreement and Gwet's AC2. RESULTS: Observations using the AUTUSI atlas demonstrated almost perfect-to-perfect interexaminer agreement (percentage agreement ranged from 96% to 100%, and Gwet's AC2 values ranged from 0.81 to 1.00) and moderate-to-perfect intraexaminer agreement (percentage agreement ranged from 67% to 100%, and Gwet's AC2 values ranged from 0.54 to 1.00). CONCLUSION: The AUTUSI atlas demonstrated excellent intraexaminer and interexaminer reproducibility for evaluating first MTPJ joint effusion, synovial hypertrophy, synovitis, joint space narrowing, osteophytes, and cartilage thickness. The AUTUSI atlas affords an opportunity to detect prognostic markers of OA earlier in the disease cascade and has the potential to advance understanding of the pathologic process of first MTPJ 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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".