Test–Retest Reliability and Sensitivity to Change of <scp>Ultrasound‐Based</scp> Methods of Measuring Synovial Inflammation in Knee Osteoarthritis
Bibliographic record
Abstract
OBJECTIVES: To assess test-retest reliability of musculoskeletal ultrasound (US) measures of inflammation in patients with knee osteoarthritis (OA) and to assess the sensitivity to change of US measures of inflammation in patients with knee OA. METHODS: To mimic a common clinical scenario, 36 patients (n = 70 knees) with symptomatic knee OA who were in stable condition underwent 2 assessments within 14 days by different operators and different US machines, graded by a single rater. Test-retest reliability was measured using Cohen's kappa coefficient, intraclass correlation coefficient (ICC), and absolute agreement parameters. A total of 51 patients (n = 72 knees) were tested immediately before and 21-28 days after intraarticular glucocorticoid injection to investigate sensitivity to change and longitudinal construct validity. Paired t-tests and standardized response mean (SRM) were used to assess sensitivity to change. Multivariate linear regression was used to investigate longitudinal construct validity of US with Knee Injury and Osteoarthritis Outcome Score (KOOS) pain scores, while adjusting for covariates. RESULTS: 0.71, 0.92). US measures of synovitis and effusion demonstrated low-to-moderate sensitivity to change (SRM -0.29, -0.50). The associations between changes in US measures and KOOS pain scores over time were low, and 95% confidence intervals included zero. CONCLUSION: In a clinical setting, US measures of inflammatory features of knee OA have substantial reliability and low-to-moderate sensitivity to change, whereas measures of structural OA features are less reliable. Longitudinal construct validity of US measures of synovitis and effusion to KOOS pain scores is not strongly supported.
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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 | 0.025 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".