Physicians’ Perspectives on the Diagnosis and Treatment of Chronic Nonbacterial Osteomyelitis
Bibliographic record
Abstract
Background/Purpose . Understanding the practices of pediatric rheumatologists in diagnosing and treating chronic nonbacterial osteomyelitis (CNO) can provide important information to guide the development of consensus treatment plans. The objectives of this study were to determine physicians’ approaches to (1) diagnosing and monitoring CNO, (2) ordering a bone biopsy, and (3) making treatment decisions. Methods . A survey was distributed among members of the Childhood Arthritis and Rheumatology Research Alliance using a web-based questionnaire. Results . 121 of 277 (41%) attending physician members completed the survey. Plain radiographs (89%) were most commonly used followed by regional MRI (78%), bone scintigraphy (43%), and whole-body MRI (36%). The top three reasons for performing a biopsy were constitutional findings (66%), unifocal bone lesions (64%), and nocturnal bone pain (45%). Nearly all responders (95%) prescribed nonsteroidal anti-inflammatory drugs (NSAIDs) as initial therapy. For patients who failed NSAID treatment, methotrexate (67%), tumor necrosis factor inhibitors (65%), and bisphosphonates (46%) were the next most commonly used treatments. The presence of a spinal lesion increased the use of bisphosphonate treatment. Conclusion . The diagnostic approach and disease activity monitoring for CNO varied among surveyed physicians. Our survey findings provided important background for the development of consensus treatment plans for CNO.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".