Appendicitis while on alectinib for non-small cell lung cancer: a tale of two case reports
Bibliographic record
Abstract
Introduction: Aberrant expression of anaplastic lymphoma kinase (ALK) is found in 3%-7% of patients with non-small cell lung cancer (NSCLC). Alectinib is a tyrosine kinase inhibitor used as first-line treatment targeting ALK-positive tumors. We herein report two cases of appendicitis highlighting it as a rare, possible adverse event of treatment with alectinib. Case presentation: The first case is a 60-year-old woman with a previous history of stage 1 lobular breast cancer and early-stage lung cancer treated with segmentectomy, subsequently presenting with ALK-positive advanced NSCLC. Treatment with alectinib resulted in partial response, but she developed gastrointestinal symptoms that were assessed with computed tomography (CT) of the abdomen revealing right lower quadrant stranding without appendiceal visualization. Her symptoms continued despite an antibiotic course with re-imaging concerning for acute appendicitis, which was successfully treated with appendectomy and amoxicillin-clavulanic acid. The second case is a previously healthy 58-year-old man with advanced ALK-positive NSCLC who was started on first-line treatment with alectinib and subsequently diagnosed with asymptomatic acute appendicitis on re-staging CT abdomen. Signs on CT resolved with amoxicillin-clavulanic acid. Definitive treatment was conducted with a delayed elective appendectomy. Both patients remained on alectinib over the courses of appendicitis without interruption. Conclusion: While appendicitis has not been previously described as an adverse effect of alectinib, its incidence in two patients at our center within several months following the administration of alectinib raises its suspicion as a possible adverse effect.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 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".