Increasing Cases of Chronic Nonbacterial Osteomyelitis in Children: A Series of 215 Cases From a Single Tertiary Referral Center
Bibliographic record
Abstract
OBJECTIVE: Chronic nonbacterial osteomyelitis (CNO) is a rare autoinflammatory bone disease that is gaining recognition from clinicians and researchers. We aim to publish data from our cohort of patients with CNO living in the northwestern United States to increase the awareness of specific demographics, characteristics, and presentation of this rare disease. METHODS: A retrospective chart review was performed of our electronic medical records. Patients with complete chart records who met criteria for a diagnosis of CNO from 2005 to 2019 were included. Extracted data including patient demographics, bone biopsy results, and lesion locations on advanced imaging were analyzed. King County census data were used to calculate the annual new case rate within our center. RESULTS: A total of 215 CNO cases were diagnosed at our large tertiary pediatric hospital. The majority of cases were of White race residing in Washington's most populous county, King County. Most cases were diagnosed in 2016 to 2019, showing a significant increase in the annual case rate from 8 to 23 per million children in King County, though there did not appear to be a seasonal predilection. Biopsy rate decreased from 75% to 52%. One hundred fifty-two (71%) children had family history of autoimmunity. With increasing use of whole-body magnetic resonance imaging (WB-MRI), results showed 68% had multiple lesions. CONCLUSION: CNO has been diagnosed at an increased rate in recent years. WB-MRI may assist in identifying other lesions that may be asymptomatic on presentation. Bone biopsy is still required in some children at the time of diagnosis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".