Cancer Treatment–Related Bone Loss: A Review and Synthesis of the Literature
Bibliographic record
Abstract
Cancer therapy can result in significant bone loss and increased risk of fragility fracture. Chemotherapy, aromatase inhibitors, and gonadotropin-releasing hormone analogues contribute to increases in the rate of bone remodelling and reduce bone mineral density. Patients with prostate cancer on androgen deprivation therapy experience an increase in the risk of fracture. New research has demonstrated the key role played by bisphosphonates in preventing declines in bone density and increases in bone remodelling. Novel antiresorptive agents targeting receptor activator of nuclear factor κB ligand have great potential in skeletal protection and prevention of bone loss related to cancer therapy. Early assessment of skeletal health, followed by initiation of calcium, vitamin D, and an exercise program are valuable in the prevention and treatment of osteoporosis. In addition, individuals at increased risk for fracture should be offered antiresorptive therapy. Early data have demonstrated that bisphosphonates are able to prevent the bone loss and increased bone remodelling associated with cancer therapy, including aromatase inhibition and androgen deprivation therapy. The present paper reviews the new research and advances in the management of bone loss associated with both cancer therapy and estrogen deficiency in the postmenopausal female.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".