Effect of Oral Bisphosphonates for Osteoporosis on Development of Skeletal Metastases in Women With Breast Cancer: Results From a Pharmaco-Epidemiological Study
Bibliographic record
Abstract
BACKGROUND: Treatment with bisphosphonates in women with breast cancer and established bone metastasis delays further skeletal-related events. Evidence is emerging that bisphosphonates are beneficial for secondary prevention of bone metastasis. The study aimed to estimate the effect of oral bisphosphonates for treatment or prevention of osteoporosis on development of bone metastasis in a population of women with breast cancer. METHODS: A historical cohort of 21664 women diagnosed with breast cancer was created from health administrative data in Quebec, Canada. The primary outcome was time to develop bone metastasis; exposure was bisphosphonate use prediagnosis, postdiagnosis, both, or neither and a cumulative index of drug exposure. The sample was stratified according to stage (0-II or III) at time of diagnosis. Cox proportional hazards tested the effect of bisphosphonate use on time to develop bone metastases. RESULTS: Taking bisphosphonates postdiagnosis of breast cancer only or continuing bisphosphonates started prior to diagnosis after diagnosis was associated with a reduction in risk of bone metastasis from 45% to 28% in women with local disease at diagnosis. In women with regional disease, postdiagnosis bisphosphonate use, with or without prediagnosis use, reduced risk by almost 50%. A statistically significant dose-response trend was observed relating increased use to lower risk (slope = 0.94, 95% confidence interval = 0.90 to 0.99). Bisphosphonates were also associated with a decreased risk of all-cause mortality similar to that of the development of bone metastasis. CONCLUSION: Low-dose oral bisphosphonates administered for prevention or treatment of postmenopausal osteoporosis were associated with lower risk of skeletal metastasis in patients with early- or more advanced-stage 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 | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".