Management of RANKL-mediated Disorders With Denosumab in Children and Adolescents: A Global Expert Guidance Document
Bibliographic record
Abstract
CONTEXT: Denosumab is an effective treatment for many receptor activator of nuclear factor kappa-B ligand (RANKL)-mediated disorders but there are potential safety considerations and limited data to guide its use in children and adolescents. OBJECTIVE: This document seeks to summarize the evidence and provide expert opinion on safe and appropriate use of denosumab in pediatric RANKL-mediated disorders. PARTICIPANTS: Ten experts in pediatric bone and mineral medicine from 6 countries with experience in the use of denosumab participated in the creation of this document. EVIDENCE: Data were sourced from the published literature, primarily consisting of case reports/series and review articles because of the lack of higher level evidence. Expert opinion of the authors was used substantially when no published data were available. CONCLUSION: Denosumab is an effective treatment for RANKL-mediated disorders in children and adolescents but is often not curative and, in some cases, is best used in conjunction with surgical or other medical treatments. Careful multidisciplinary planning is required to define the goals of treatment and expert oversight needed to manage the risk of mineral abnormalities. Substantive, collaborative research efforts are needed to determine optimal treatment regimens and minimize risks.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".