Pembrolizumab-Induced Insulin-Dependent Diabetes Mellitus in a Patient With Triple-Negative Breast Cancer: A Rare Immune-Related Adverse Event
Bibliographic record
Abstract
A 68-year-old female patient with a background of triple-negative breast carcinoma on pembrolizumab with no history of diabetes presented to the emergency department with fatigue, polyuria, nausea, dizziness, shortness of breath, dry mouth, and increased thirst. She had recently received the third dose of the second cycle of neoadjuvant combination chemotherapy and immunotherapy (pembrolizumab/carboplatin/paclitaxel) and was due to receive the next dose. Initial assessment revealed hyperglycemia with ketosis without acidosis. The patient was treated with fluid resuscitation and insulin infusion under the diabetic ketoacidosis (DKA) guidelines of the hospital and was eventually transitioned to a basal-bolus insulin regimen, which was continued after discharge. Based on the temporal relationship between pembrolizumab therapy and the onset of diabetes, along with the patient's persistent insulin dependence, a diagnosis of immune checkpoint inhibitor-induced diabetes mellitus (ICI-DM) was established. The patient has clinically improved, chemotherapy and immunotherapy have been discontinued, and surgical intervention is planned. This case highlights the importance of recognizing ICI-DM as a rare immune-related adverse event in patients who receive immunotherapy with programmed cell death protein-1 (PD-1)/programmed cell death ligand-1 (PD-L1) inhibitors.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".