Cross-sectional reliability and concurrent validity of a quantitative 2-dimensional ultrasound image analysis of effusion and synovial hypertrophy in knee osteoarthritis
Bibliographic record
Abstract
Objective: Effusion-synovitis is related to pain and progression in knee osteoarthritis (OA), but current gold standard ultrasound (US) measures are limited to semi-quantitative grading of joint distension or 1-dimensional thickness measures. A novel quantitative 2-dimensional image analysis methodology is applied to US images of effusion-synovitis; reliability and concurrent validity was assessed in patients with knee OA. Methods: ) of total synovitis, effusion and hypertrophy components were exported. Intra-rater reliability and test-retest reliability (1-14 days washout) were estimated with intra-class correlation coefficients (ICCs). Concurrent validity was measured by Spearman correlations between quantitative measures and gold standard OMERACT and caliper measurements of synovitis. Results: ). Correlation between total synovitis area and OMERACT grade was 0.84, 0.81 between total synovitis area and effusion-synovitis calipers, and 0.81 between total effusion area and effusion calipers. Conclusion: This new research tool for image analysis demonstrated excellent intra-rater reliability, good concurrent validity, and moderate test-retest reliability. Quantitative 2D US measures of effusion-synovitis and its individual components may enhance the study and management of knee OA.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".