Immune checkpoint inhibitors and risk of immune-mediated adverse events: a cohort study comparing extended versus standard interval administration
Bibliographic record
Abstract
The COVID-19 pandemic precipitated the implementation of extended interval immune checkpoint inhibitors (ICIs) in an effort to limit hospital visits, but few studies have examined their safety. This study aimed to compare in oncology outpatients, immune-mediated adverse events (IMAEs) in terms of total number, incidence, severity, and time to occurrence, based on exposure to standard or extended interval ICIs. A retrospective cohort study was conducted in patients who received at least one dose of an ICI between 2015 and 2021. Data were collected from patient records and pharmacy software. Adjusted logistic, Poisson, and Cox regression models were estimated. A total of 310 patients with a mean age of 67.1 years were included, 130 of whom had the extended interval. No statistically significant differences were observed between the groups. With the standard and extended intervals, the mean total number of IMAE per participant was 1.02 and 1.18, respectively; the incidence of an IMAE was 62% and 64%. Of the 147 IMAE episodes in the standard interval group, 14 (9.5%) were grade 3 or higher, while there were 15 (12.4%) among the 121 IMAE episodes in the extended interval group. Compared with standard interval, the use of extended interval did not increase the risk of having a first IMAE (adjusted hazard ratio 0.92 (95% CI 0.67-1.26)). This study suggests that the administration of an ICI according to extended interval is as safe as the administration according to standard interval in oncology outpatients.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".