EULAR/ACR classification criteria for paediatric chronic nonbacterial osteomyelitis (CNO)
Bibliographic record
Abstract
OBJECTIVES: To develop and validate classification criteria for paediatric chronic nonbacterial osteomyelitis (CNO) jointly supported by the European Alliance of Associations for Rheumatology (EULAR) and the American College of Rheumatology (ACR). METHODS: This international initiative had 4 phases: (1) candidate items were proposed in a survey of paediatric rheumatologists, (2) criteria definition and reduction by Delphi and nominal group technique exercises, (3) criteria weighting using multicriteria decision analysis, and (4) refinement of weights and threshold score in a development cohort of 441 patients and validation in another cohort of 514 patients. RESULTS: The new EULAR/ACR classification criteria for CNO require typical radiographic or magnetic resonance imaging findings and bone pain as an obligatory entry criterion and exclusion criteria of malignancy, infection, vitamin C deficiency, and hypophosphatasia, followed by additive weighted criteria in 5 clinical (site of bone lesions, pattern of bone lesions, age at onset, coexisting conditions, fever) and 4 pathology/laboratory domains (bone biopsy findings if done, anaemia, C-reactive protein level, and erythrocyte sedimentation rate). A total score ≥55 is required for classification as CNO. The new criteria had a sensitivity of 82% and specificity of 98% in the validation cohort. CONCLUSIONS: These new classification criteria for paediatric CNO developed with international input reflect current views about CNO, have high specificity and good sensitivity, and provide a key foundation for future CNO research.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".