Prescription medications and breast cancer : how do bisphosphonates and omeprazole affect a woman’s risk of developing breast cancer?
Bibliographic record
Abstract
Background: Breast cancer is the most commonly diagnosed cancer in women. It is a heterogeneous disease having a number of risk factors that vary by menopausal status and disease subtype. There is evidence that some pharmaceutical medications may affect breast cancer risk. This thesis investigated bisphosphonates, which are used to treat osteoporosis, as well as omeprazole, a proton pump inhibitor used to treat gastroesophageal reflux disease, and their association with breast cancer and breast cancer subtypes. Methods: This study utilized questionnaire information provided by participants in the Canadian Breast Cancer Study, a case-control study conducted jointly in Vancouver, British Columbia and Kingston, Ontario. Data from the British Columbia arm of the study was linked to PharmaNet, an administrative pharmaceutical database that records prescriptions dispensed in British Columbia. Multiple imputation and logistic regression were used to model the association between the prescription medications and breast cancer while adjusting for confounders. Results: There was not enough evidence to suggest an association between omeprazole and breast cancer, considered either as a whole (OR: 1.02; 95% CI: 0.68-1.52) or by estrogen receptor status (p-heterogeneity = 0.94). However, there was evidence to suggest long-term bisphosphonate use (>1025 cumulative Defined Daily Doses) was associated with a decreased risk of invasive breast cancer, relative to no bisphosphonate use (OR: 0.65; 95% CI: 0.45-0.94). The protective effect was not evident for situ disease (OR: 0.89; 95% CI: 0.36-2.19). There was no difference in risk observed by estrogen receptor status (p-heterogeneity = 0.83). Conclusions: This research showed a protective effect for invasive breast cancer with long-term bisphosphonate use, but the results must be interpreted cautiously due to the potential for confounding by indication. This potential bias has been mitigated somewhat by adjusting for factors strongly associated with bone density, such as BMI, but does not completely eliminate the possibility. The study also had limited power to investigate risk with breast cancer subtypes. In the case of both omeprazole and bisphosphonates, further investigation is warranted to elucidate their relationship with breast cancer.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".