Quantification of small joint space width, periarticular bone microstructure and erosions using high-resolution peripheral quantitative computed tomography in rheumatoid arthritis.
Bibliographic record
Abstract
OBJECTIVES: This paper aims to investigate the ability of a novel imaging technique, high-resolution peripheral quantitative computed tomography (HR-pQCT), to quantify joint space width at the metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints, and provide periarticular bone microstructure measurements (including volumetric density and morphometric indices). We also compared the sensitivity and specificity of HR-pQCT to detect erosions relative to plain radiography. METHODS: HR-pQCT imaging of the MCP and PIP joints of the dominant hand was performed in 30 rheumatoid arthritis (RA) and control subjects matched for age, sex and dominant hand use. The joint space width was calculated by determining the number of voxels between three-dimensional images of the articular surfaces. Periarticular bone microstructure was quantified for the 2nd and 3rd MCP joints using standard analysis. The presence of erosions was confirmed by viewing both two- and three-dimensional images of the joints. RESULTS: Quantitative measures of joint space width and periarticular bone microstructure were obtained with precision. Although not powered to detect differences between RA and control subjects, we identified a trend to narrowing of the 2nd MCP joints in RA (mean difference 250 μm, p=0.057). RA erosions most frequently occurred at the metacarpal head of the MCP joint, and HR-pQCT identified erosions in 24.7% more joints compared to plain radiography. CONCLUSIONS: This is the first study to exploit the quantitative capabilities of HR-pQCT to provide joint space width measurements at the MCP and PIP joints. We provide further proof that HR-pQCT improves erosion detection and yields reproducible periarticular bone microstructure measurements.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".