Novel Ultrasound Image Acquisition Protocol and Scoring System for the Pediatric Knee
Bibliographic record
Abstract
OBJECTIVE: The use of musculoskeletal ultrasound is increasing among pediatric rheumatologists. Reliable scoring systems are needed for the objective assessment of synovitis. The aims of this study were to create a standardized and reproducible image acquisition protocol for B-mode and Doppler ultrasound of the pediatric knee, and to develop a standardized scoring system and determine its reliability for pediatric knee synovitis. METHODS: Six pediatric rheumatologists developed a set of standard views for knee assessment in children with juvenile arthritis. Subsequently, a comprehensive literature review, practical exercises, and a consensus process were performed. A scoring system for both B-mode and Doppler was then developed and assessed for reliability. Interreader reliability or agreement among a total of 16 raters was determined using 2-way single-score intraclass correlation coefficient (ICC) analysis. RESULTS: Twenty-one views to assess knee arthritis were initially identified. Following completion of practical exercises and subsequent consensus processes, 3 views in both B-mode and Doppler were selected: suprapatellar longitudinal and medial/lateral parapatellar transverse views. Several rounds of scoring and modifications resulted in a final ICC of suprapatellar view B-mode 0.89 (95% confidence interval [95% CI] 0.86-0.92) and Doppler 0.55 (95% CI 0.41-0.69), medial parapatellar view B-mode 0.76 (95% CI 0.68-0.83) and Doppler 0.75 (95% CI 0.66-0.83), and lateral parapatellar view B-mode 0.82 (95% CI 0.75-0.88) and Doppler 0.76 (95% CI 0.66-0.84). CONCLUSION: A novel B-mode and Doppler image acquisition and scoring system for assessing synovitis in the pediatric knee was successfully developed through practical exercises and a consensus process. Study results demonstrate overall good-to-excellent reliability.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".