Aplastic anemia secondary to dual cancer immunotherapies a physician nightmare: case report and literature review
Bibliographic record
Abstract
BACKGROUND: Treatment with immune checkpoint inhibitors has revolutionized cancer treatment over the past several years. Despite their clinical benefits, a wide range of immune-mediated toxicities can be observed including hematological toxicities. Although, the majority can easily be managed, immune-mediated adverse events rarely can be severe and difficult to approach. Herein, we are reporting a case of very severe aplastic anemia secondary to ipilimumab (I) and nivolumab (N) treatment that failed various treatment including intensive immune suppressive therapy. CASE PRESENTATION: We described a case of a 45-year old white male, heavy smoker presented to the clinic complaining of left flank pain. He was found to have a metastatic renal cell carcinoma for which he was treated with dual immunotherapy and later complicated by severe immune related adverse events. The patient later died after failing intensive immune suppressive therapy. CONCLUSION: Immunotherapy has become an established pillar of cancer treatment improving the prognosis of many patients with variant malignancies. Yet, lethal adverse events can occur in rare cases. It is our duty, as physicians, to remain alert and cautious.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".