Quantitative and Semiquantitative Bone Erosion Assessment on High-resolution Peripheral Quantitative Computed Tomography in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To develop novel quantitative and semiquantitative bone erosion measures at metacarpophalangeal (MCP) and wrist joints in patients with rheumatoid arthritis (RA) using high-resolution peripheral quantitative computed tomography (HR-pQCT), and to correlate these measurements with disease duration and bone marrow edema (BME) patterns derived from magnetic resonance imaging (MRI). METHODS: Sixteen patients with RA and 7 healthy subjects underwent hand and wrist HR-pQCT and 3-Tesla MRI. Bone erosions of the MCP2, MCP3, and distal radius were evaluated by measuring maximal erosion dimension on axial slices, which is a simple and fast measurement, and then were graded (grades 0-3) based on the maximal dimension. Correlation coefficients were calculated between (1) sum maximal dimensions, highest grades, and sum grades of bone erosions; (2) erosion measures and the clinical evaluation; (3) erosion measures and BME volume in distal radius. RESULTS: The inter- and intrareader agreements of maximal erosion dimensions were excellent (intraclass correlation coefficients 0.89, 0.99, and root mean square error 9.4%, 4.7%, respectively). Highest grades and sum grades were significantly correlated to sum maximal dimensions of all erosions. Number of erosions, sum maximal erosion dimensions, highest grades, and sum grades correlated significantly with disease duration. Number of erosions, sum maximal dimensions, and erosion grading of the distal radius correlated significantly with BME volume. CONCLUSION: HR-pQCT provides a sensitive method with high reader agreement in assessment of structural bone damage in RA. The good correlation of erosion measures with disease duration as well as BME volume suggests that they could become feasible measures of erosions in RA.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".