Durvalumab for Extensive-Stage of Small-Cell Lung Cancer With Lambert-Eaton Myasthenic Syndrome
Bibliographic record
Abstract
Durvalumab is an immune checkpoint inhibitor (ICI) of anti-programmed cell death protein 1 ligand antibody. ICI-combined chemotherapy has recently become a standard regimen for extensive-stage of small-cell lung cancer (ES-SCLC). SCLC is well known to be the most likely tumor associated with Lambert-Eaton myasthenic syndrome (LEMS), a rare autoimmune disease of a neuromuscular junction disorder. Although LEMS has been reported to be induced by ICI as immune-mediated adverse events, it remains unknown whether ICI can deteriorate preexisting paraneoplastic syndrome (PNS) of LEMS. Our rare case was successfully treated by durvalumab plus chemotherapy without exacerbation of preexisting PNS of LEMS. We report a 62-year-old female with ES-SCLC and preexisting PNS of LEMS. She started carboplatin-etoposide in combination with durvalumab. This immunotherapy achieved nearly complete response. However, multiple brain metastases were found after two courses of maintenance durvalumab. Her symptoms and physical examinations of LEMS improved despite of no significant change in compound muscle action potential amplitude in the nerve conduction study. The titer of anti-P/Q-type voltage-gated calcium channel (VGCC) antibody decreased from 1,419.2 to 263.5 pmol/L during the immunotherapy. In conclusion, ICI in combination with platinum doublet chemotherapy is still challenging but may be a treatment option for ES-SCLC patients complicated with PNS of LEMS.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".