Real-World Adherence to Toxicity Management Guidelines for Immune-Related Adverse Events
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) affect immunologic homeostasis, leading to immune-related adverse events (irAEs). Early irAE detection and management can prevent significant morbidity and mortality. A retrospective chart review was performed to characterize irAEs associated with nivolumab, ipilimumab, and nivolumab plus ipilimumab in adult medical oncology patients in Nova Scotia Health-Central Zone from 2013-2020, and to describe adherence to toxicity management guidelines. Diarrhea/colitis, hepatitis, pneumonitis, nephrotoxicity, and cardiotoxicity were studied. Of 129 charts reviewed, 67 patients (51.9%) experienced at least one irAE for a total of 98 irAEs and a 1.5% fatality rate. Of these irAEs, 33.7% led to an emergency room visit. Patients were admitted to hospital and steroids were used in 24.5% and 35.7% of cases, respectively. In 17.3% of irAEs, ICIs were permanently discontinued. In 20.4% of irAEs, ICIs were held, and patients were monitored; while in 18.4%, ICIs were held until the irAE was Grade 0-1 (and until steroids were tapered). Almost 47% of irAEs were managed according to guidelines (14.3% were not), and 38.8% had no documented management. Patients receiving immunotherapy frequently experience irAEs with half of irAEs having documented management adhering to guidelines. As immunotherapy indications expand, it is important to ensure irAEs are documented and managed appropriately.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".