Reliability of the Pediatric Specific Musculoskeletal Ultrasound Scoring Systems for the Elbow, Wrist, and Finger Joints
Bibliographic record
Abstract
OBJECTIVE: Musculoskeletal ultrasound (MSUS) is increasingly being used in the evaluation of pediatric musculoskeletal diseases. In order to provide objective assessments of arthritis, reliable MSUS scoring systems are needed. Recently, joint-specific scoring systems for arthritis of the pediatric elbow, wrist, and finger joints were proposed by the Childhood Arthritis and Rheumatology Research Alliance (CARRA) MSUS workgroup. This study aimed to assess the reliability of these scoring systems when used by sonographers with different levels of expertise. METHODS: Members of the CARRA MSUS workgroup attended training sessions for scoring the elbow, wrist, and finger. Subsequently, scoring exercises of B mode and power Doppler (PD) mode still images for each joint were performed. Interreader reliability was determined using 2-way single-score intraclass correlation coefficients (ICCs) for synovitis and Cohen [Formula: see text] for tenosynovitis. RESULTS: Seventeen pediatric rheumatologists with different levels of MSUS expertise (1-15 yrs) completed a 2-hour training session and calibration exercise for each joint. Excellent reliability (ICC > 0.75) was found after the first scoring exercise for all the finger and elbow views evaluated on B mode and PD mode, and for all of the wrist views on B mode. After a second training session and a scoring exercise, the wrist PD mode views reached excellent reliability as well. CONCLUSION: The preliminary CARRA MSUS scoring systems for assessing arthritis of the pediatric elbow, wrist, and finger joints demonstrate excellent reliability among pediatric MSUS sonographers with different levels of expertise. With further validation, this reliable joint-specific scoring system could serve as a clinical tool and scientific outcome measure.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".