Cancer Treatment–Induced Bone Loss in Breast and Prostate Cancer
Bibliographic record
Abstract
PURPOSE: Bone loss resulting from the treatment of breast and prostate cancer is an emerging problem. Bisphosphonates have a potential role in the prevention of this cancer treatment-induced bone loss (CTIBL). METHODS: Studies evaluating the incidence and prevalence of CTIBL in early breast and prostate cancer patients and trials evaluating the preventative role of bisphosphonates were identified by a search of the PubMed and Cochrane Library databases through the end of March 2008. Reference lists from retrieved articles were cross referenced, and further information was obtained from relevant scientific meetings. RESULTS: Several therapies commonly used in the treatment of women and men with breast and prostate cancers, in particular the aromatase inhibitors (AIs) for breast cancer and androgen deprivation therapy (ADT) for prostate cancer, are associated with significant bone loss and with an increase in fracture risk. The use of bisphosphonates seems to attenuate the bone loss, although the long-term impact remains unclear because of insufficient follow-up. CONCLUSION: Adjuvant endocrine therapy with an AI or androgen deprivation can be considered a risk factor for the development of osteopenia, osteoporosis, and bone fracture, which can be mitigated by appropriate bisphosphonate therapy. Clear identification of risk factors for osteoporosis in individual patients should aid treatment decisions about whether to use bisphosphonates when starting or switching to an AI or ADT. Patients need to be educated about this risk and other measures to avoid this complication, including lifestyle modifications that may benefit their general and bone health.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".