Pulmonary Nocardiosis in a Non-Small Cell Lung Cancer Patient Being Treated for Pembrolizumab-Associated Pneumonitis
Bibliographic record
Abstract
Introduction: Immune-check-point inhibitors (ICIs) are established in the treatment of many malignancies. Many immune-related adverse events (irAEs) are well described; however, there is less information about opportunistic infections in cancer patients receiving ICIs. Case Presentation: We describe the case of a 62-year-old woman with non-small cell lung cancer, who relapsed after surgical resection and chemotherapy. She received 13 months of pembrolizumab, achieving stable disease, before presenting with suspected pneumonitis 2 weeks prior to departure for an international vacation. She was treated with high-dose corticosteroids and, shortly thereafter, developed severe nocardiosis, requiring venovenous extracorporeal membrane oxygenation and lengthy hospitalization. Conclusion: To our knowledge, this represents the second known case of pulmonary nocardiosis in a patient on pembrolizumab. Moreover, this is a rarely reported instance of opportunistic bacterial infection following steroid treatment for ICI pneumonitis. This case report emphasizes the risk of bacterial infection associated with ICI pneumonitis, both due to the difficulty of excluding underlying infection at presentation, and the immunosuppression caused by irAE treatment. As such, we suggest that clinicians maintain a high suspicion for potential infection in ICI pneumonitis, and strongly consider initiating infectious workup with regular follow-ups for monitoring. Prophylactic antibiotics could be considered when such monitoring is not possible.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".