Autoimmune Diabetes Associated With Pembrolizumab: A Review of Published Case Reports
Bibliographic record
Abstract
The utility of immunotherapy, such as pembrolizumab, is becoming essential in the treatment of certain cancers. Pembrolizumab works through binding of programmed cell death 1 receptor that blocks the binding of the programmed cell death ligand 1 and is commonly used in non-small cell lung cancer and melanoma. Pembrolizumab has been reported to be associated with multiple adverse reactions such as pneumonitis, colitis, hepatitis, hypophysitis, hyperthyroidism, hypothyroidism, nephritis, and type 1 diabetes; however, pembrolizumab causing type 1 diabetes was only reported in 0.1% of the patients in clinical trials. A review of the literature generated 1,001 unique citations of which six reported cases of autoimmune diabetes associated with pembrolizumab were selected and compared. Review of the cases showed no sexual predilection and the average age of onset was 58 years old. The majority of the patients were treated for melanoma (5/6 cases), initially presented with diabetic ketoacidosis (4/6 cases), and had at one point taken ipilimumab (4/6 cases). There was no association found between the number of treatments received and the development of diabetes. With the increasing use of pembrolizumab in cancer treatment regular blood glucose monitoring during treatment, especially in patients who had also taken ipilimumab, may prevent the onset of this life-threatening complication.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".