Dynamic knee joint function in children with juvenile idiopathic arthritis (JIA)
Bibliographic record
Abstract
BACKGROUND: Juvenile idiopathic arthritis (JIA) is a chronic illness with a high risk of developing long-term disability. Disease activity is currently being monitored and quantified by ACR core set. Here, joint inflammation is determined; however joint function is the crucial component for developing disability. The aim of this study was to quantify and compare dynamic joint function in healthy and arthritic knee joints and to evaluate response to improvement. METHODS: A single center cohort study of consecutive children presenting to the rheumatology outpatient clinic was performed to measure dynamic knee joint function. Serial measures were performed if possible. Splint fixed electrogoniometers were used to measure dynamic knee joint function including ROM and flexion and extension torque. RESULTS: A total of 54 children were tested including 44 with JIA, of whom eight had to be excluded for non-JIA-related knee problems. The study included 36 JIA patients of whom eight had strictly unilateral knee arthritis, and nine controls. Dynamic joint function ROM and torque depended on age and bodyweight, as demonstrated in healthy joints. ROM and torques were significant lower in arthritic compared to unaffected knee joints in children with unilateral arthritis and across the cohort. Importantly, extension torque was the most sensitive marker of impaired joint function. Follow up measurements detected responsiveness to change in disease activity. CONCLUSIONS: Measuring dynamic joint function with electrogoniometers is feasible and objective. Active ROM and torque during flexion and extension of arthritic knee joints were significant lower compared to unaffected. In dynamic joint measurement extension torque is a sensitive marker for disease activity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".