Ultrasonographic Measurements of Joint Cartilage Thickness in Healthy Children: Age- and Sex-Related Standard Reference Values
Bibliographic record
Abstract
OBJECTIVE: Loss of joint cartilage may be an early feature of chronic inflammatory joint diseases like juvenile idiopathic arthritis (JIA). Conventional radiography usually detects only late changes such as joint space narrowing and bone erosion rather than early inflammatory changes. Joint cartilage is easily visualized with high-frequency ultrasonography (US), but age- and gender-related normal standard reference values should be established before US measurement of cartilage thickness becomes standard procedure in the clinic. METHODS: A cross-sectional study of bilateral grey-scale US cartilage thickness of the knee, ankle, wrist, and second metacarpophalangeal (MCP) and second proximal interphalangeal (PIP) joints was performed in 394 (215 boys/179 girls) healthy Danish Caucasian children aged between 7 and 16 years. RESULTS: Cartilage thickness differed significantly between sexes (p < 0.001 for all joints), boys having thicker cartilage than girls. Cartilage thickness clearly decreased with increasing age in both sexes. A formula for calculating sex-specific cartilage thickness at different ages in childhood is suggested. No difference between the right and left side of the investigated joints was observed. CONCLUSION: Using US, we established age- and sex-related normal reference intervals for cartilage thickness of the knee, ankle, wrist, and MCP and PIP joints in 7- to 16-year-old children, and designed a formula for calculating hyaline cartilage thickness in all age groups throughout childhood.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".