The OMERACT Ultrasound Task Force — Status and Perspectives
Bibliographic record
Abstract
This article reports the most recent work of the Outcome Measures in Rheumatology (OMERACT) Ultrasound Task Force, and highlights the future research priorities discussed at the OMERACT 10 meeting. Results of the following studies were presented: (1) intra- and interobserver reliability of ultrasound detecting and scoring synovitis in different joints of patients with rheumatoid arthritis (RA); (2) systematic review of previous ultrasound scoring systems of synovitis in RA; (3) enthesitis systematic review and Delphi definition exercise in spondyloarthritis enthesitis; (4) enthesitis intra- and interobserver reliability exercise; and (5) Delphi definition exercise in hand osteoarthritis, and reliability exercises. Study conclusions were discussed, and a future research agenda was approved, notably further validation of an OMERACT ultrasound global synovitis score (GLOSS) in RA, emphasizing the importance of testing feasibility, predictive value, and added value over standard clinical variables. Future research areas will include validating scoring systems for enthesitis and osteoarthritis, and testing the metric qualities of ultrasound for evaluating tenosynovitis and structural damage in RA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.076 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".