Stage 4 Non-small Cell Lung Cancer With Human Epidermal Growth Factor Receptor 2 Alterations and Myocarditis Induced by Immune Checkpoint Inhibitors: A Case Report
Bibliographic record
Abstract
Immune checkpoint inhibitor (ICI)-induced myocarditis is one of the most serious and potentially fatal toxicities of immunotherapy. Most of the guidelines for managing this toxicity are based on expert opinions. Human epidermal growth factor receptor 2 (HER2) alterations in non-small cell lung cancer (NSCLC) could be found using next-generation sequencing (NGS) on tissue and liquid biopsies. There is an approved first-line targeted therapy for HER2-positive breast and gastroesophageal cancers. Until now, no first-line targeted therapy for NSCLC with HER2 alterations has been approved. This case report presents a patient with metastatic HER2 NSCLC with a high PD-L1 level. She was started on first-line single-agent immunotherapy pembrolizumab. She tolerated the first two cycles well. Before the third cycle, she had palpitations and was tachycardiac. Furthermore, investigations found raised troponin levels. She was diagnosed with ICI-induced myocarditis. After being admitted to the cardiac care unit (CCU) and beginning pulse steroid treatment, she responded well with decreasing troponin levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| 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".