Which joints and why do rheumatologists scan in rheumatoid arthritis by ultrasonography? A real life experience.
Bibliographic record
Abstract
OBJECTIVES: Ultrasonography (US) has been demonstrated to improve assessment of synovitis and disease activity in rheumatoid arthritis (RA). However, the utility and feasibility of US in RA in clinical practice in real life is not known. We aimed to investigate: i) the indications for performing US in RA in daily practice; and ii) whether the number of scanned joints varies according to the purpose. METHODS: Consecutive patients who had a US scan either for diagnosis or follow-up for RA from 5 centres were recruited. The sonographers were asked to mark the joints that had a US scan and grade their findings. Descriptive analysis was applied to find out the sites and the number of joints scanned and compared according to the indications of US. RESULTS: Two hundred consecutive patients were recruited. The most common indication was assessing disease activity (48.5%) followed by diagnosis (45.5 %). Wrists (66%) and MCPs (63.5) were the most frequently scanned joints followed by knees (26%), PIPs (20%). The number of joints scanned by US was significantly higher when performed for diagnostic purposes as compared to assessing disease activity and guidance for injections (p=0.001). CONCLUSIONS: The current data highlight differences between the numbers of joints for which that the clinician feels necessary to perform US in real life. This observation may be a guide when providing recommendations regarding which joints need to be scanned according to the indication.
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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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".