Detecting new erosions in rheumatoid arthritis over one year – Radiography and high-resolution computed tomography of finger joints
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To compare the number of new erosions in two metacarpophalangeal (MCP) joints over one year assessed by high-resolution peripheral quantitative computed tomography (HR-pQCT) and conventional radiography (CR). Furthermore, to estimate the diagnostic accuracy of erosive progression by CR with HR-pQCT as the reference standard. METHODS: Paired sets of image data were available from patients with RA (n=310), who underwent HR-pQCT and CR, including the 2nd and 3rd MCP joints only of the dominant hand at baseline and at the one-year follow-up. Erosion number was determined using HR-pQCT and CR. Erosive volume was estimated from segmented HR-pQCT images, and erosion scores were obtained by the Sharp/van der Heijde method from CR. Erosive progression was defined as an increase in total erosion number or a study-specified increase in total erosive volume or total erosion score. RESULTS: At baseline, 250 erosions were identified by CR compared to 1864 erosions by HR-pQCT. After one year, 3 new erosions were detected by CR compared to 66 new erosions by HR-pQCT. Erosive progression was identified in 40 patients using HR-pQCT and in 3 patients using CR. With HR-pQCT as reference, CR had a sensitivity of 2.5% (95% CI: 0.1-13.2%) and a specificity of 99.3% (95% CI: 97.3-99.9%) for detecting erosive progression. CONCLUSION: HR-pQCT identified more than 20 times the number of new erosions, and more than 10 times as many patients with erosive progression than CR. HR-pQCT is a sensitive tool for monitoring new erosions and erosive progression over one year in RA.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it