Definitions for the Sonographic Features of Joints in Healthy Children
Bibliographic record
Abstract
OBJECTIVE: Musculoskeletal ultrasonography (US) has potential in the assessment of disease activity and structural damage in childhood arthritides. In order to assess pathology, the US characteristics of joints in healthy children need to be defined first. The aim of this study was to develop definitions for the various components of the normal pediatric joint. METHODS: The definitions were developed by an expert group and applicability was assessed on a collection of standardized scans of the knee and ankle joints by scoring the scans on a Likert scale. The definitions were then modified and applicability was reassessed before sending the definitions for approval to a larger panel of experts. A final scoring on stored images of all relevant joints at different ages followed. RESULTS: Five definitions were developed addressing the articular bone, cartilage, joint capsule, epiphyseal ossification center, and synovial membrane. In total, 224 US images of knees and ankles were acquired, of which 172 were selected for scoring. An agreement of ≥80% was not met for any of the definitions, but after modifications, 81-97% agreement was reached. This version of the definitions was approved by 15 US experts. In the final validation exercise, all definitions reached an agreement of ≥80% for the shoulder, elbow, wrist, metacarpophalangeal hip, knee, ankle and metatarsophalangeal joint. CONCLUSION: US definitions for the normal pediatric joint were successfully developed through a Delphi process and validated in a practical exercise. These results provide the basis to develop definitions for pathology and to support the standardized use of US in pediatric rheumatology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".