Expert consensus recommendations for the diagnosis and treatment of chronic non-bacterial osteitis (CNO) in adults
Bibliographic record
Abstract
BACKGROUND: There is considerable practice variation in labelling, diagnosis and treatment of adults with sterile bone inflammation. We developed a expert consensus recommendations on the disease definition, diagnosis and treatment of this rare condition. METHODS: Systematic literature review and Grading of Recommendations, Assessment, Development and Evaluations-based appraisal of evidence, two Delphi surveys and three digital and in-person consensus meetings with a multidisciplinary expert panel and patient representatives. RESULTS: A consensus disease definition was developed and the term 'chronic non-bacterial osteitis' (CNO) is proposed to describe adults with sterile bone inflammation. For initial imaging evaluation of adults with suspected CNO, the panel recommends MRI or otherwise CT combined with nuclear imaging. Whole-body imaging at initial evaluation can be considered for diagnostic and prognostic purposes. Suggested first-line treatment in adults with active CNO includes non-steroidal anti-inflammatory drugs/cyclooxygenase 2-inhibitors. Second-line treatment preferably consists of intravenous bisphosphonates, and otherwise tumour necrosis factor-α inhibitors. Choice between them should be individualised, considering the presence of additional inflammatory features. The panel further discusses outcome measures, follow-up and management of adverse events and complications. CONCLUSIONS AND FUTURE PERSPECTIVES: These expert consensus recommendations are intended to support healthcare professionals worldwide in their care for adults with CNO. They also lay the groundwork for establishing international patient registries, translational research lines and multicentre trials, all of which are urgently required.
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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 | 0.110 | 0.223 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".