Six-joint ultrasound in rheumatoid arthritis: a feasible approach for implementing ultrasound in remission.
Bibliographic record
Abstract
OBJECTIVES: Subclinical disease activity in rheumatoid arthritis (RA) detected by imaging methods is predictive for flares and damage. Lack of time is the major limitation for not screening for subclinical disease in routine practice. We aimed to determine the most feasible protocol to screen patients with no clinical disease activity by ultrasound (US). METHODS: A hundred consecutive RA patients with no clinical activity according to the physician had an US scan for 38 joints. The prevalence of power Doppler (PD) signal in each joint was determined and different combinations of joints were assessed for their ability to capture this information. The most practical combination with a good sensitivity was tested in another group of 50 RA patients. RESULTS: Having any PD signal was not linked to the disease activity parameters whereas presence of PD of ≥2 was associated with higher DAS28CRP. Sixty patients had at least one joint with PD of grade ≥2 (60%). A combination of the wrists and 2nd-3rd MCP joints bilaterally (PD-6 joints) was able to detect 45/60 (75%) cases with PD signals and 45% of the whole patient population. The correlation between PD-38 and PD-6 joints was excellent (r=0.820, p<0.0001). PD-6 joints in the 2nd cohort was also able to detect 22/50 (44%) of the whole group. CONCLUSIONS: Subclinical disease activity could be detected in 60% of RA patients when 38 joints screened by US. Limiting the screening to wrists, 2nd-3rd MCPs bilaterally was acceptable as it detected 75% of cases with subclinical disease and increased the feasibility.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".