Immune Checkpoint Inhibitors: A Pediatric Needs Assessment
Notice bibliographique
Résumé
Objectives Immune checkpoint inhibitor (ICI) therapy is increasing in pediatric oncology. Immune-related adverse events (irAEs) can affect various organ systems, including rheumatic-irAEs (Rh-irAEs) such as inflammatory arthritis, myositis, sicca syndrome, systemic lupus erythematosus, sarcoidosis, and vasculitis.[1] Few cases report detail Rh-irAEs and their management in the pediatric population.[2] Our objective was to assess the familiarity of pediatric rheumatologists (PR) worldwide with ICI-induced Rh-irAEs, gauge their confidence managing these conditions, and identify knowledge gaps to guide future educational efforts. Methods We circulated a 21-question online survey to 2084 PR 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 responses were received of which 55 (80%) were PR from academic centers, and 9 (13%) were from community practices (Table 1). 24 (35%) had >20 years of clinical experience. Despite global distribution, 56 (81%) of responses came from North America. 34 (49%) of respondents were not aware of ICIs and their related mechanisms, indications, and side effects, and 40 (58%) were not familiar with irAEs. 55 (80%) had never managed a patient with Rh-irAEs. Among those who had (14/69), the median number of cases managed was 2.75 (IQR 1.75). Confidence in managing these conditions was limited: 39 (57%) were “not confident at all” managing Rh-irAEs, 34 (49%) were “not confident at all” managing pre-existing autoimmune diseases (PAD) in ICI users, and 46 (67%) were “not confident at all” advising oncology colleagues on initiating or discontinuing ICIs in the context of Rh-irAEs or PADs. No one felt “completely confident” managing these conditions. Several knowledge gaps were identified: 59 (86%) in long-term management, 55 (80%) in acute management, and 51 (74%) in recognition and diagnosis. 43 (62%) indicated the need for pediatric-specific clinical guidelines. Of the 14 (20%) respondents with clinical experience treating Rh-irAEs, initial treatment approaches varied, with 4/14 (29%) using NSAIDs, 3/14 (21%) using prednisone, and 4/14 (29%) combining prednisone with methotrexate. Long-term management also varied, with 5/14 (36%) using methotrexate, and 3/14 (21%) using TNF inhibitors. Table 1: Respondent Demographics, Knowledge & Confidence Assessment Conclusion Significant knowledge gaps and a lack of confidence exist among PR in managing ICI-related Rh-irAEs. As ICI use increases in pediatric oncology, PR exposure to Rh-irAEs will follow. Targeted educational programs and clinical guidelines will be valuable to address these gaps and improve patient care. [1.] Ghosh N. Rheum Dis Clin North Am 2022; 48(2):411-28. [2.] Storwick JA. Pediatr Rheumatol Online J 2024;22(1):49.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».