Exposure to oral bisphosphonates and risk of cancer
Bibliographic record
Abstract
Recently, oral bisphosphonate use has increased markedly in the United States and elsewhere. Little is known about cancer risks associated with these drugs. A few studies have observed associations between bisphosphonates and the risk of breast, colorectal and esophageal cancer. However, the risk of all cancer and the risk of other cancers have not been investigated. In our study, we examined the risk of all cancer and site specific cancers in individuals taking bisphosphonates. Data were extracted from the UK General Practice Research Database to compare site-specific cancer incidence in a cohort of oral bisphosphonate users and a control cohort. Hazard ratios (HRs) were calculated using Cox regression modeling. The bisphosphonate and control cohort contained 41,826 participants (mean age 70, 81% female). Overall, the bisphosphonate cohort compared with the control cohort had a reduced risk of all cancer after any bisphosphonate usage [HR=0.87, 95% confidence interval (CI) 0.82, 0.92]. In the bisphosphonate cohort, compared with the control cohort, there was no evidence of a difference in the risk of lung (HR=1.03, 95% CI 0.88, 1.20) or prostate cancer (HR=0.86, 95% CI 0.67, 1.09) but breast (HR=0.71, 95% CI 0.62, 0.81) and colorectal cancer (HR=0.74, 95% CI, 0.60-0.91) were both reduced. Our findings indicate that bisphosphonates do not appear to increase cancer risk. Although reductions in breast and colorectal cancer incidence were observed in bisphosphonate users it is unclear, particularly for breast cancer, to what extent confounding by low bone density may explain the association.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".