Antihistamine use and breast cancer risk
Bibliographic record
Abstract
Antihistamines are structurally similar to DPPE, a tamoxifen derivative known to promote tumor growth, and to antidepressants. Animal experiments have linked certain antihistamines and antidepressants with enhanced tumor growth in mice. The few epidemiologic studies examining antihistamine use have not indicated an increased risk. In light of suggestive animal data, structural similarities between antihistamines and DPPE, the widespread use of antihistamines, and the lack of epidemiologic investigation into their use and breast cancer risk, it is important to examine this issue. Female cases aged 25-74 years, diagnosed 1996 to 1998, were identified through the Ontario Cancer Registry. Controls were a random, age-matched sample of women. Cases (n=3,133) and controls (n=3,062) completed a mailed questionnaire that included questions about antihistamines used regularly (undefined), type and duration. Age-adjusted odds ratio (OR) estimates and 95% confidence intervals (CIs) were obtained using logistic regression. Antihistamine users were at no increased risk for breast cancer (OR=0.93, 95% CI: 0.81, 1.06), and no trend in risk was observed for age starting or duration of use. Antihistamine users were at no increased risk. No confounding or effect modification was identified in multivariate modeling. Our findings do not support the hypothesis that women who use antihistamines are at a greater breast cancer risk than those who do not.
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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 | 0.000 | 0.001 |
| 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".