Prospective Clinical Trials to Advance the Study of Immune Checkpoint Inhibitor Toxicity
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) are a class of drug that produces durable and sustained anti-tumour responses in a wide variety of malignancies. The exponential rise in their use has been mirrored by a rise in immune-related adverse events (IrAEs). Knowledge of such toxicities, as well as effective management algorithms for these toxicities, is essential to optimize clinical efficacy and safety. Currently, the guidelines for management of the IrAEs are based largely on retrospective studies and case series. In this article, we review the current landscape of clinical trials investigating the management of IrAEs with an aim to develop standardised, randomised controlled trial-based management algorithms for ICI-related toxicities.
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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 | 0.029 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 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".