Clinical care of children with sterile bone inflammation
Bibliographic record
Abstract
PURPOSE OF REVIEW: To review the current literature of sterile bone inflammation in childhood and to evaluate the evidence for clinical care including diagnostic methods and treatment. RECENT FINDINGS: Chronic noninfectious osteomyelitis includes several different entities marked by sterile bone inflammation associated with histologic evidence of a predominant neutrophil infiltration in the absence of autoantibodies and autoreactive T cells, some of which are associated with a genetic mutation. Whole body MRI is helpful in detecting asymptomatic lesions. Initial treatment with NSAIDs is usually sufficient to control symptoms as the bone heals. However, if the lesions persist and do not respond to first-line treatment, or involve the spine or hip, treatment with bisphosphonate will usually lead to a resolution of symptoms. Rarely, treatment with anti-TNF agents is required. SUMMARY: This review summarizes recent information on diagnosis, treatment and prognosis of disorders involving sterile bone inflammation in childhood. It also addresses the evolving differential diagnosis for autoinflammatory disorders that include sterile bone inflammation and presents a treatment algorithm for management.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".