A population-based case-control study of Selective Serotonin Reuptake Inhibitors (SSRIs) and breast cancer: The impact of duration of use, cumulative dose and latency
Bibliographic record
Abstract
BACKGROUND: Selective serotonin reuptake inhibitors (SSRIs), a popular class of antidepressants, may increase breast cancer risk by stimulating the secretion of prolactin, a potential tumour promoter. We evaluated the effects of duration of SSRI use, cumulative dose, and latency on the risk of breast cancer by conducting a population-based case-control study utilizing Saskatchewan health databases. METHODS: Cases included 1,701 women with primary invasive breast cancer diagnosed from 2003 to 2006, and controls consisted of 17,017 women, randomly selected from the population registry. Use of SSRIs was compiled using the Saskatchewan prescription database. Unconditional logistic regression was conducted to evaluate the impact of duration of combined SSRI use (total number of prescriptions dispensed), cumulative dose (total dosage received) and timing of use (two or more years, two to seven years and more than seven years prior to index date) on the risk of breast cancer. RESULTS: Overall, SSRI use was not associated with an increased risk of breast cancer regardless of our definition of cumulative use (total number of prescriptions dispensed and total dosage). In addition, our results indicate that prolonged SSRI use does not have a latent effect on breast cancer risk. Also, our findings are not suggestive of an increased risk of breast cancer with the use of individual SSRIs. CONCLUSIONS: Our study improved upon most previous studies by having a longer follow-up period, a larger sample size of long-term SSRI users and consideration of risk during specific exposure time windows that take latency into account. Given the potential health benefits of using SSRIs, our results suggest that the issue of breast cancer risk may no longer be a concern for women requiring long-term SSRIs.
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 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".