Antidepressant medication use and breast cancer risk: a case-control study
Bibliographic record
Abstract
BACKGROUND: Animal and human studies have reported an association between antidepressant (AD) medication use and breast cancer risk. A population-based case-control study was designed specifically to examine this association among women in Ontario, Canada. METHODS: The Ontario Cancer Registry (OCR) identified women diagnosed with primary breast cancer. Controls, randomly sampled from the female population of Ontario, were frequency matched by 5-year age groups. A mailed self-administered questionnaire included questions about lifetime use of AD and potential confounders. Multivariate logistic regression yielded odds ratio estimates. RESULTS: 'Ever' use of AD was reported by 14% (441/3077) cases versus 12% (372/2994) controls. The age-adjusted odds ratio (AOR) for 'ever' use was 1.17, (95% CI: 1.01, 1.36). An increased risk was also observed for selective serotonin reuptake inhibitors = 1.33 (95% CI: 1.07, 1.66), Sertraline = 1.58 (95% CI: 1.03, 2.41), and Paroxetine = 1.55 (95% CI: 1.00, 2.40). None of the 30 variables assessed for confounding altered the risk estimate by more than 10%. Multivariate adjustment including all possible breast cancer risk factors yielded an unchanged, but not significant, point estimate (MVOR = 1.2, 95% CI: 0.96, 1.51). No relationship was observed for duration or timing of AD use. CONCLUSIONS: A modest association between 'ever' use of AD and breast cancer was found using the most parsimonious multivariate model. OR estimates did not change, but CI were widened and statistical significance lost, after adjustment for factors associated with breast cancer risk.
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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 | 0.003 | 0.007 |
| 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".