Use of Antidepressant Medications and the Possible Association with Breast Cancer Risk
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it