Immune checkpoint inhibitors and the pediatric rheumatologist: a pediatric needs assessment
Bibliographic record
Abstract
BACKGROUND: The use of immune checkpoint inhibitor (ICI) therapy is increasing in pediatric oncology. ICIs can cause rheumatic-immune related adverse events (Rh-irAEs) such as inflammatory arthritis and myositis. Few case reports detail Rh-irAEs and their management in the pediatric population. Our objective was to assess the familiarity of pediatric rheumatologists (PRs) worldwide with Rh-irAEs, gauge confidence in managing these conditions, and identify knowledge gaps to guide future educational efforts. METHODS: We circulated an online survey to 2084 PRs via the "Dr. Peter Dent Pediatric Rheumatology Bulletin Board." Responses were collected from June 2024 to September 2024. We collected data on practitioner demographics, knowledge of ICIs and Rh-irAEs, confidence in managing Rh-irAEs, and preferred educational resources. RESULTS: Sixty-nine participants responded, of which 55 (80%) were PRs from academic centers. Despite global distribution, 56 (81%) responses came from North America. Thirty-four (49%) respondents were not aware of ICIs and their related mechanisms, indications, and side effects, and 40 (58%) were not familiar with irAEs. Fifty-five (80%) had never managed a patient with Rh-irAEs. Among those who had (14/69, 21%), the median number of cases managed was 2.0 (IQR 0.0). Thirty-nine respondents were "not confident at all" managing Rh-irAEs, 34 were "not confident at all" managing pre-existing autoimmune diseases (PAD) in ICI users, and 46 were "not confident at all" advising oncology colleagues on initiating or discontinuing ICIs in the context of Rh-irAEs or pre-existing autoimmune diseases (PAD). No respondents felt "completely confident" managing these conditions. Participants identified knowledge gaps in long-term management, acute management, and recognition and diagnosis. Forty-three indicated the need for pediatric-specific clinical guidelines. Of the 14 respondents with clinical experience treating Rh-irAEs, treatment varied, with 4 using nonsteroidal anti-inflammatory drugs, 3 using prednisone, and 4 combining prednisone with methotrexate. Long-term management also varied, with 5 using methotrexate, and 3 using tumor necrosis factor inhibitors. CONCLUSIONS: Significant knowledge gaps and a lack of confidence exist among PRs managing ICI-related Rh-irAEs. As ICI use increases in pediatric oncology, PRs' exposure to Rh-irAEs will follow. Targeted educational programs and clinical guidelines may be valuable to address these gaps.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".