Enhancing Care Through a Virtual Canadian Community of Practice for Managing Immune-Related Adverse Events
Bibliographic record
Abstract
The advent of immune checkpoint inhibitors (ICIs) has significantly transformed cancer treatment outcomes. However, these therapies can induce immune-related adverse events (irAEs) that may affect any organ system, sometimes requiring specialized expertise. As ICIs are increasingly used across various tumor types and in earlier treatment settings, not all practitioners have the necessary support network to handle complex irAEs. To address this gap, we collaborated with ONCOassist, a leading app for oncology professionals, to establish the first virtual Canadian Community of Practice (CoP) focused on irAEs. The CoP facilitates continuous learning and improves patient care among Canadian clinicians treating patients with immunotherapy by providing a platform for knowledge exchange and peer-to-peer support. This article outlines the development and growth of the CoP on irAEs, highlighting both successes and challenges. As of May 2024, over a year since its inception, the CoP on irAEs has attracted almost 130 Canadian oncology healthcare professionals, and peer-to-peer interactions and engagement continue to increase. To ensure its long-term sustainability, we plan to evolve and adapt the CoP to meet the needs of the oncology community and address clinical challenges associated with new therapies.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".