Use of Antidepressant Medications and the Possible Association with Breast Cancer Risk
Bibliographic record
Abstract
BACKGROUND: Antidepressant medication use has dramatically increased over the past decade, particularly for the newer classes such as selective serotonin reuptake inhibitors. While there is no question about the usefulness of these medications, it is important to review animal and epidemiologic studies that have evaluated the association between antidepressant medication use and the risk of breast cancer. METHODS: This paper reviews the scientific literature pertaining to the prevalence of and indications for antidepressant medication use, and the possible association between antidepressant medication use and breast cancer risk. RESULTS: Antidepressant medications are most commonly indicated for depressive disorders, and are also used for other conditions (e.g., anxiety disorders, personality disorders, and pain). In addition, antidepressants may be an effective alternative to estrogen therapy for the alleviation of hot flashes among peri-/postmenopausal women. Several epidemiologic studies have reported that certain antidepressants may be associated with a slightly increased breast cancer risk; however, the literature remains inconsistent. CONCLUSIONS: The possibility of an association between certain antidepressant medications and breast cancer risk has not been excluded, although further studies are needed before the body of scientific evidence can be conclusive. Evidence to date does not support a change in the current use of antidepressant medications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".