Detecting new erosions in rheumatoid arthritis over one year – Radiography and high-resolution computed tomography of finger joints
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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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".