A case of severe Pembrolizumab-induced neutropenia
Bibliographic record
Abstract
Immune checkpoint inhibitors have revolutionized cancer therapy. Given their mechanism of action, immune-related adverse events have been associated with their use. We present the first documented case of pembrolizumab-induced grade IV neutropenia. A 73-year-old women known for myositis, Crohn's disease, and hypothyroidism and diagnosed with PD-L1 positive stage IV pulmonary adenocarcinoma is treated with Pembrolizumab. She develops grade IV neutropenia 2 weeks after her second infusion. She is therefore hospitalized and treated initially with corticosteroids, granulocyte colony-stimulating factor, and intravenous immunoglobulins. Given the persistent neutropenia, cyclosporine was added, but quickly stopped owing to fever. The patient recovered her neutrophils 6.5 weeks after her initial Pembrolizumab infusion and 12 days after admission. She has been subsequently successfully tapered off steroids with no recurrence after 3 months of follow-up. This is the first case of grade IV neutropenia secondary to Pembrolizumab. This case is of particular interest given the patient's pre-existing autoimmune history. Treatment of severe neutropenia due to other PD1 inhibitors has generally consisted of steroids, granulocyte colony-stimulating factor, intravenous immunoglobulins, mycophenolate mofetil, cyclosporine A, and anti-thymocyte globulins - though the benefits of immunosuppression are not clear and may be harmful given the infectious risks. Large studies are required to clarify the spectrum and optimal management of immune-related adverse events and overall risk/benefits of immune checkpoint inhibitors in patients with pre-existing autoimmunity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".