Cone Beam Computed Tomography for Assessment of Erosions in Early Rheumatoid Arthritis: A Pilot Study
Bibliographic record
Abstract
OBJECTIVE: Cone beam computed tomography (CBCT) can accurately assess erosive disease in the hands, wrists, and feet in established rheumatoid arthritis (RA). The aim of this study was to compare CBCT with conventional radiography (CR) for the assessment of erosions in patients with early RA. METHODS: CBCT and CR of the hands, wrists, and feet of 17 patients with treatment-naive early RA were assessed at diagnosis and at the 6-month and 12-month follow-up. Erosions on CBCT scans were scored by the same observer using the modified RA Magnetic Resonance Imaging Score, which evaluates the same joints as the Sharp/van der Heijde score (SHS). Radiographs were scored for erosions using the SHS by the same observer. RESULTS: At baseline, there was a significant difference in the erosion score between CBCT and CR, as shown with a percentage of maximum scores. The number of erosions and the number of eroded joints were significantly higher with CBCT compared with CR at 6 and 12 months. The number of detected repair of erosions was higher with CBCT than with CR at both 6 and 12 months. CONCLUSION: CBCT was more sensitive than CR in detecting erosions and repair in patients with early RA. CBCT has the potential to become a sensitive tool for monitoring destructive disease in patients with 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".