The Current Understanding of the Endocrine Effects From Immune Checkpoint Inhibitors and Recommendations for Management
Why this work is in the frame
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Bibliographic record
Abstract
Clinical trials in the past decade have established the antitumor effects of immune checkpoint inhibition as a revolutionary treatment for cancer. Namely, blocking antibodies to cytotoxic T-lymphocyte antigen 4 and programmed death 1 or its ligand have reached routine clinical use. Manipulation of the immune system is not without side effects, and autoimmune toxicities often known as immune-related adverse events (IRAEs) are observed. Endocrine IRAEs, such as hypophysitis, thyroid dysfunction, and insulin-dependent diabetes mellitus, can present with unique profiles that are not seen with the use of traditional chemotherapeutics. In this Review, we discuss the current hypotheses regarding the mechanism of these endocrinopathies and their clinical presentations. Further, we suggest guidelines and algorithms for patient management and future clinical trials to optimize the detection and treatment of immune checkpoint-related endocrinopathies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it