Effect of Age on Rheumatic Immune-Related Adverse Events: Experience From the Canadian Research Group of Rheumatology in Immuno-Oncology (CanRIO)
Bibliographic record
Abstract
Objective Immune checkpoint inhibitors (ICIs) have revolutionized cancer outcomes but are limited by immune-related adverse events (irAEs), including rheumatic irAEs (Rh-irAEs). Aging is associated with increased inflammation, referred to as "inflammaging." In this study, we explore the effect of age on severity, frequency, and treatment of Rh-irAEs. Methods Adults with new Rh-irAEs after ICI exposure are followed prospectively across 10 Canadian sites as part of the Canadian Research Group of Rheumatology in Immuno-Oncology (CanRIO) prospective cohort. In this study of patients seen between January 2020 and March 2023, we compare the severity of Rh-irAEs and number of irAEs between patients aged ≥ 65 years and < 65 years and explore potential epidemiologic, treatment-related, and phenotypic differences between the older and younger patients. Results A total of 139 patients with de novo Rh-irAEs were included, 58 in the younger (aged < 65 yrs) and 81 in the older (aged ≥ 65 yrs) group. There were no significant differences in severity of Rh-irAEs (P= 0.84) or number of irAEs (P= 0.21), although there was a nonsignificant trend toward more younger patients than older patients with ≥ 3 irAEs (24% vs 14%). Types of treatment for Rh-irAEs were similar between the groups. ICI continuation did not differ. Within the ICI-related inflammatory arthritis subgroup, there was also no significant difference in the incidence of severe Rh-irAEs (P= 0.51). Conclusion Similar numbers of overall irAEs and severity of Rh-irAEs were observed between older vs younger patients who developed Rh-irAEs after treatment with ICI therapy, suggesting that inflammaging does not play a significant role in Rh-irAEs. Larger studies are needed to explore potential differences in patient phenotypes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".