Reliability assessment of ultrasound muscle echogenicity in patients with rheumatic diseases: Results of a multicenter international web-based study
Bibliographic record
Abstract
Objectives: To investigate the inter/intra-reliability of ultrasound (US) muscle echogenicity in patients with rheumatic diseases. Methods: Forty-two rheumatologists and 2 radiologists from 13 countries were asked to assess US muscle echogenicity of quadriceps muscle in 80 static images and 20 clips from 64 patients with different rheumatic diseases and 8 healthy subjects. Two visual scales were evaluated, a visual semi-quantitative scale (0-3) and a continuous quantitative measurement ("VAS echogenicity," 0-100). The same assessment was repeated to calculate intra-observer reliability. US muscle echogenicity was also calculated by an independent research assistant using a software for the analysis of scientific images (ImageJ). Inter and intra reliabilities were assessed by means of prevalence-adjusted bias-adjusted Kappa (PABAK), intraclass correlation coefficient (ICC) and correlations through Kendall's Tau and Pearson's Rho coefficients. Results: The semi-quantitative scale showed a moderate inter-reliability [PABAK = 0.58 (0.57-0.59)] and a substantial intra-reliability [PABAK = 0.71 (0.68-0.73)]. The lowest inter and intra-reliability results were obtained for the intermediate grades (i.e., grade 1 and 2) of the semi-quantitative scale. "VAS echogenicity" showed a high reliability both in the inter-observer [ICC = 0.80 (0.75-0.85)] and intra-observer [ICC = 0.88 (0.88-0.89)] evaluations. A substantial association was found between the participants assessment of the semi-quantitative scale and "VAS echogenicity" [ICC = 0.52 (0.50-0.54)]. The correlation between these two visual scales and ImageJ analysis was high (tau = 0.76 and rho = 0.89, respectively). Conclusion: The results of this large, multicenter study highlighted the overall good inter and intra-reliability of the US assessment of muscle echogenicity in patients with different rheumatic diseases.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".