Confidence Check: Closing the Educational Gaps in Immune Checkpoint Inhibitors
Notice bibliographique
Résumé
Objectives The growing use of immune checkpoint inhibitors (ICIs) in pediatric oncology has introduced pediatric rheumatologists (PR) to immune-related adverse events (irAEs), including rheumatic-irAEs (Rh-irAEs).[1] These novel conditions prompt a need for learning around their presentation and complex management issues, especially due to the lack of pediatric-specific guidelines. Currently, there is limited understanding of PRs’ familiarity with ICIs and Rh-irAEs, as well as their educational needs. This study aimed to identify these gaps and explore PRs’ preferred learning formats to inform future educational offerings. Methods Eight learning-related questions were incorporated into a 21-question online survey assessing PRs’ knowledge of Rh-irAEs. The survey was distributed to 2,084 PRs globally via the “Dr. Peter Dent Pediatric Rheumatology Bulletin Board,” and responses were collected between June and September 2024. Results A total of 69 responses were received, 55 (80%) from academic centers and 9 (13%) from community practices. Despite global outreach, 56 (81%) responses were from North America (Table 1). 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. Knowledge gaps were identified by participants in the following areas: long-term management (86%, 59/69), acute management (80%, 55/69), and in recognition and diagnosis (74%, 51/69). 43/69 (62%) indicated the need for pediatric-specific clinical guidelines. Awareness of existing educational resources was limited: 39/69 (57%) were unaware of the EULAR Guidelines for managing irAEs, 65 (94%) were unaware of CanRIO’s learning modules or case rounds, and 33 (48%) were unaware of any educational resources. Interest in learning was high, with 63 (91%) expressing willingness to participate in educational activities. The preferred formats were online modules, podcasts, or webinars (64%), self-directed learning (49%), and group-scheduled activities (43%). Conference-based content was favored over local content (55% vs 26%), and only 6 participants showed no interest. Table 1: Respondent Demographics, Knowledge & Confidence Assessment Conclusion PRs are eager to learn more about ICI-induced Rh-irAEs, with a clear preference for diverse educational formats. Future steps include designing and implementing educational activities focused on these knowledge gaps and learning preferences, followed by reassessing PRs’ competency in this emerging area. [1.] Ghosh N. Rheum Dis Clin North Am 2022;48(2):411-28.
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 | 0,021 | 0,110 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,006 |
| Science ouverte | 0,002 | 0,008 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,002 |
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 ».