Bone Health in Patients with Breast Cancer: Recommendations from an Evidence-Based Canadian Guideline
Bibliographic record
Abstract
Bone loss is common in patients with breast cancer. Bone modifying agents (BMAs), such as bisphosphonates and denosumab, have been shown to reverse or stabilize bone loss and may be useful in the primary and metastatic settings. The purpose of this review is to provide clear evidence-based strategies for the management of bone loss and its symptoms in breast cancer. A systematic review of clinical trials and meta-analyses published between 1996 and 2012 was conducted of MEDLINE and EMBASE. Reference lists were hand-searched for additional publications. Recommendations were developed based on the best available evidence. Zoledronate, pamidronate, clodronate, and denosumab are recommended for metastatic breast cancer patients; however, no one agent can be recommended over another. Zoledronate or any oral bisphosphonate and denosumab should be considered in primary breast cancer patients who are postmenopausal on aromatase inhibitor therapy and have a high risk of fracture and/or a low bone mineral density and in premenopausal primary breast cancer patients who become amenorrheic after therapy. No one agent can be recommended over another. BMAs are not currently recommended as adjuvant therapy in primary breast cancer for the purpose of improving survival, although a major Early Breast Cancer Cooperative Trialists' Group meta-analysis is underway which may impact future practice. Adverse events can be managed with appropriate supportive care.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".