A Challenging Case of Immune-Related Organizing Pneumonitis Following Programmed Cell Death 1 Inhibitor Therapy in Non-Small Cell Lung Cancer
Bibliographic record
Abstract
The immune system plays a vital role in defending the body against infections and tumors, inspiring the development of innovative therapies like immune checkpoint inhibitors (ICIs) that have transformed the treatment of advanced cancers. Pembrolizumab, a monoclonal antibody targeting the programmed cell death 1 (PD-1) receptor, is a powerful ICI effective against various malignancies but frequently associated with immune-related adverse events (irAEs). In this report, we present a case of organizing pneumonitis that developed 3 months after initiation of pembrolizumab treatment for non-small cell lung cancer (NSCLC). A 64-year-old woman with NSCLC, undergoing maintenance therapy with pembrolizumab, presented with multiple lung consolidations. Her medical history included thalassemia minor, a pre-pyloric ulcer, hiatal hernia, and a history of smoking. Extensive microbiological testing, including bronchoalveolar lavage, was negative, and her condition did not improve with broad-spectrum antibiotics. This led to a suspected diagnosis of pembrolizumab-induced pneumonitis. Treatment with high-dose corticosteroids resulted in full clinical and radiological resolution. This case underscores the importance of monitoring for irAEs during ICI therapy, as differential diagnosis between immunotherapy-induced organizing pneumonia and tumor progression is challenging in patients with advanced lung cancer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".