Time to Update Evidence-Based Guideline Recommendations About Concurrent Tamoxifen and Antidepressant Use? A Systematic Review
Bibliographic record
Abstract
Concer ns around phar macological interaction between tamoxifen and antidepressants have resulted in evidence-base guidelines that recommend avoidance or caution with concurrent use.It remains unclear however whether this interaction is clinically important.A systematic review of studies comparing endocrine therapy (including tamoxifen and aromatase inhibitors) alone or concurrent with antidepressants in breast cancer patients was performed.The literature search sought studies within MEDLINE, EMBASE, and the Cochrane Collaboration Library published from database inception until December 1, 2020.Outcomes of interest included recurrence, breast cancer-specific survival, overall mortalit y, qualit y of life, and treatment compliance.Studies were assessed with the Cochrane Risk of Bias tool for randomized controlled trials and the Newcastle Ottawa tool for case-control and cohort studies.From 695 citations, we included 15 studies (2 randomized controlled trials [255 patients], 10 retrospective cohort studies [75,678 patients], and 3 case-control studies [18,836 patients]).While between-study clinical and methodologic differences (including analysis of confounding variables) precluded formal meta-analysis, findings from included studies did not find consistent evidence that concurrent use of antidepressants (including paroxetine) with tamoxifen therapy has negative impacts on the outcomes of interest.In this systematic review, despite data from nearly 100,000 patients, concurrent use of tamoxifen and antidepressants showed no consistent negative effect on clinical outcomes.Given the recognized harm to patients of changing either endocrine therapy or antidepressants to avoid concurrent use, current evidencebased guidelines should be updated accordingly.More rigorously designed pharmacoepidemiologic studies are needed.
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 | 0.018 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".