Definition for Rheumatoid Arthritis Erosions Imaged with High Resolution Peripheral Quantitative Computed Tomography and Interreader Reliability for Detection and Measurement
Bibliographic record
Abstract
OBJECTIVE: High-resolution peripheral quantitative computed tomography (HR-pQCT) sensitively detects erosions in rheumatoid arthritis (RA); however, nonpathological cortical bone disruptions are potentially misclassified as erosive. Our objectives were to set and test a definition for pathologic cortical bone disruptions in RA and to standardize reference landmarks for measuring erosion size. METHODS: HR-pQCT images of metacarpophalangeal joints of RA and control subjects were used in an iterative process to achieve consensus on the definition and reference landmarks. Independent readers (n = 11) applied the definition to score 58 joints and measure pathologic erosions in 2 perpendicular multiplanar reformations for their maximum width and depth. Interreader reliability for erosion detection and variability in measurements between readers [root mean square coefficient of variation (RMSCV), intraclass correlation (ICC)] were calculated. RESULTS: Pathologic erosions were defined as cortical breaks extending over a minimum of 2 consecutive slices in perpendicular planes, with underlying trabecular bone loss and a nonlinear shape. Interreader agreement for classifying pathologic erosions was 90.2%, whereas variability for width and depth erosion assessment was observed (RMSCV perpendicular width 12.3%, axial width 20.6%, perpendicular depth 24.0%, axial depth 22.2%; ICC perpendicular width 0.206, axial width 0.665, axial depth 0.871, perpendicular depth 0.783). Mean erosion width was 1.84 mm (range 0.16-8.90) and mean depth was 1.86 mm (range 0.30-8.00). CONCLUSION: We propose a new definition for erosions visualized with HR-pQCT imaging. Interreader reliability for erosion detection is good, but further refinement of selection of landmarks for erosion size measurement, or automated volumetric methods, will be pursued.
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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 | 0.055 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".