Summary Findings of a Systematic Literature Review of the Ultrasound Assessment of Bone Erosions in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Bone erosions in rheumatoid arthritis (RA) have been studied in an increasing amount of research. Both earlier and present classification criteria of RA contain erosions as a significant classification component. Ultrasound (US) can detect bone changes in accessible surfaces. Therefore, the study group performed a systematic literature review of assessment of RA bone erosions with US. METHODS: A systematic search of PubMed and Embase was performed. Data on the definitions of RA bone erosions, their size, scoring, relation to synovitis, comparators, and elements of the OMERACT (Outcome Measures in Rheumatology Clinical Trials) filter were collected and analyzed. RESULTS: The selection process identified 58 original research papers. The assessed joints were most frequently metacarpophalangeal (MCP; 41 papers), proximal interphalangeal (19 papers), and metatarsophalangeal joints (MTP; 18 papers). The OMERACT definition of RA bone erosion on US was used most often (17 papers). Second and fifth MCP and fifth MTP were recommended as target joints. Conventional radiography was the most frequently used comparator (27 papers), then magnetic resonance imaging (17 papers) and computed tomography (5 papers). Reliability of assessment was presented in 20 papers and sensitivity to change in 11 papers. CONCLUSION: This paper presents results of a systematic literature review of bone erosion assessment in RA with US. The survey suggests that US can be a helpful adjunct to the existing methods of imaging bone erosions in RA. It analyzes definitions, scoring systems, used comparators, and elements of the OMERACT filter. It also presents recommendations for a future research agenda based on the results of the review.
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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 | 0.041 | 0.165 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.031 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".