Confidence Check: Closing the Educational Gaps in Immune Checkpoint Inhibitors
Bibliographic record
Abstract
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.
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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 | 0.021 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".