Autofeedback from Ultrasound Images Provides Rapid Improvement in Palpation Skills for Identifying Joint Swelling in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Joint swelling, an important factor in the classification criteria and disease activity assessment in rheumatoid arthritis (RA), renders joint palpation a necessary skill for physicians. Ultrasound (US) examination that visualizes soft tissue abnormalities is now used to assess musculoskeletal disease. We assessed the usefulness of US assessments in enhancing physical joint examination skills. METHODS: We examined 1944 joints (bilateral shoulder, elbow, wrist, metacarpophalangeal joints 1-5, and knee joints) in 108 patients with RA during April-July 2011. We first physically examined and confirmed joint swelling; subsequently, the same rheumatologist conducted US examinations and multiple assessors graded the joint swelling. When the 2 results differed, we received autofeedback from the US results to improve the physical examination skills. RESULTS: The sensitivities and specificities of physical examination for US-detected swollen joint, the correlation coefficient (CC) of the swollen joint counts, and the concordance rate in each patient for joint swelling sites and power Doppler (PD)-positive sites with the κ coefficients between the physical and US examinations were compared over time. We found that the sensitivity of physical examination increased by 42 percentage points (pp), while the specificity decreased by 18 pp. The average CC in June-July was greater than that in April-May. The percentage of κ coefficients > 0.8 increased from 8.8% to 17% for joint swelling and from 8.3% to 14% for PD-positive sites. CONCLUSION: Our results suggest that autofeedback from US assessment provides quick improvement in palpation skills for identifying joint swelling in patients with 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".