Antidepressant Medication Use and Breast Cancer Risk
Bibliographic record
Abstract
Experimental and epidemiologic studies suggest that antidepressant medication use may be associated with breast cancer risk. This hypothesis was investigated using a population-based case-control study; cases diagnosed in 1995-1996 were identified using the Ontario Cancer Registry, and controls were randomly sampled from an Ontario Ministry of Finance database. Data were collected using a self-administered questionnaire, and multivariate logistic regression was used to estimate odds ratios and 95% confidence intervals. Adjusted odds ratio estimates ranged from 0.7 to 0.8 and were not statistically significant for "ever" use of antidepressants, tricyclics, and selective serotonin reuptake inhibitors. Compared with no antidepressant use, use of tricyclic antidepressants for greater than 2 years' duration was associated with an elevated risk of breast cancer (odds ratio (OR) = 2.1, 95% confidence interval (CI): 0.9, 5.0). Of the six most commonly reported antidepressant medications, only paroxetine use was associated with an increase in breast cancer risk (OR = 7.2, 95% CI: 0.9, 58.3). Results from this study do not support the hypothesis that "ever" use of any antidepressant medications is associated with breast cancer risk. Use of tricyclic medications for greater than 2 years, however, may be associated with a twofold elevation, and use of paroxetine may be associated with a substantial increase in breast cancer risk.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".