Managing Select Immune-Related Adverse Events in Patients Treated with Immune Checkpoint Inhibitors
Bibliographic record
Abstract
The increased use of immune checkpoint inhibitors (ICIs) across cancer programs has created the need for standardized monitoring and management of immune-related adverse events (irAEs). Delayed recognition without appropriate treatment can have serious and life-threatening consequences. The management of irAEs presents a unique set of challenges that must be addressed at a multidisciplinary level. Although various national and international guidelines and working groups provide high-level recommendations for the management of irAEs, practical guidance is lacking. Furthermore, timely collaboration between specialists requires institutional protocols that enable the early recognition, assessment, and treatment of irAEs. Such protocols should be developed by institution specialists and include algorithms for all healthcare providers involved in the care of patients treated with ICIs. At William Osler Health System in Brampton, Ontario, practical step-by-step multidisciplinary treatment approaches with recommendations for the management of irAEs were developed in collaboration with experts across Canada. Here, we provide an in-depth description of the approaches, outlining baseline investigations prior to the initiation of ICIs, as well as the monitoring and management of irAEs based on symptoms, severity, and involved organ systems. We encourage other centres to adapt and modify our approaches according to their specific needs and requirements.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".