936 Assessing educational needs for immune-related adverse events curriculum in clinical practice
Bibliographic record
Abstract
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
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".