Must antidepressants be avoided in patients with neuroendocrine tumors? Results of a systematic review
Bibliographic record
Abstract
OBJECTIVE: Symptoms of depression and anxiety are common in neuroendocrine tumor (NET), yet controversy exists over whether serotonin-mediated antidepressants (SAs) are safe in this population. We sought to address this knowledge gap. METHOD: Following PRISMA guidelines, we conducted a systematic review to identify NET patients who were prescribed SA. RESULTS: We identified 15 articles, reporting on 161 unique patients, 72 with carcinoid syndrome (CS) and 89 without. There was substantial agreement between reviewers at the full-text stage (κ = 0.69). Three of the articles, all with low risk of bias, accounted for most of the cases (149/161; 93%). Among the 72 NET patients with CS prior to antidepressant usage, CS was exacerbated in 6 cases (8%), only 3 (4%) of whom chose to discontinue the antidepressant. The remaining 89 patients had no prior CS symptoms, and none developed CS following antidepressant usage. Overall, no instances of carcinoid crisis or death were reported. CONCLUSIONS: We found no evidence for serious adverse outcomes related to SA usage in NET patients. Previous authors have recommended avoiding antidepressants in NET, but our findings do not support those recommendations. Oncologists should nonetheless monitor for symptom exacerbation when prescribing SA to patients with NET.
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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 | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".