936 Assessing educational needs for immune-related adverse events curriculum in clinical practice
Notice bibliographique
Résumé
Background Immune checkpoint inhibitors (ICIs) are increasingly used to treat cancer but can lead to immune-related adverse events (irAEs) such as hepatitis, pneumonitis, or thyroiditis. While ICIs and irAEs are typically managed in an outpatient setting by oncologists, 11% of patients receiving ICIs require hospital admission for irAEs.1 While rheumatologists’ irAE management knowledge and skills have been assessed in prior studies,2 clinicians within oncology and hospital medicine have been understudied despite their relevance to the care of patients with irAEs. Methods In June and July 2022, we administered a web-based survey to University of Chicago (UChicago)-affiliated oncology providers: oncology fellows and attendings, oncology nurse practitioners (NPs) and physician assistants (PAs). We also surveyed UChicago hospitalists and medicine residents and community oncologists practicing in Chicago. We assessed current knowledge, prior experience, provider confidence, and current educational resource utilization regarding irAE diagnosis and management. We also surveyed how receptive our participants would be to future educational resources dedicated to irAE evaluation and therapy. Linear regression and logistic regression were utilized to analyze relationships between different variables. Results In total, we had a 37% response rate (171/467): highest for UChicago-affiliated oncology providers (55-59%) (table 1). Oncology attendings and fellows scored the highest on knowledge-based questions (67-68%). Higher levels of ICI and irAE experience over the past year were associated with higher levels of knowledge (OR 1.5, p<0.002). Confidence levels were also significantly associated with higher knowledge and more ICI and irAE experience (p<0.001). Almost all participants surveyed (93%) were receptive to using an online irAE resource with delineated guidelines, patient handouts, and frequently asked questions regarding irAEs. Oncology fellows and NPs/PAs were also more interested in online CME-accredited irAE sessions dedicated to irAEs than medicine residents and hospitalists (83% versus 65%). Conclusions The knowledge gaps across various groups of providers caring for patients with irAEs reflect a significant need to develop an effective didactic program aimed at enhancing knowledge and confidence for irAE evaluation and treatment. Our results support the development of an online curriculum with interactive online modules to provide case-based and simulated experiential didactics that will lead to increased knowledge and confidence in caring for patients with irAEs. References Ahern E, Allen M, Schmidt A, Lwin Z, Hughes B. Retrospective analysis of hospital admissions due to immune checkpoint inhibitor-induced immune-related adverse events (irAE). Asia Pac J Clin Oncol. 2021;17:e109–e116. Maltez N, Abdullah A, Fifi-Mah A, Hudson M, Jamal S. Checking in with immune checkpoint inhibitors: Results of a needs assessment survey of Canadian rheumatologists. J Cancer Sci Therap. 2019;2:12
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 | 0,006 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 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 ».