A Primer of Bone Metastases Management in Breast Cancer Patients
Bibliographic record
Abstract
Bone is the most common site for distant spread of breast cancer. Following a diagnosis of metastatic bone disease, patients can suffer from significant morbidity because of pain and skeletal related events (SRES). Bisphosphonates are potent inhibitors of osteoclastic function and the mainstay of bone-directed therapy for bone metastases. The aims of bisphosphonates are to prevent and delay SRES, to reduce bone pain, and to improve quality of life. Bisphosphonate therapy appears to have revolutionized treatment of bone metastases, but bisphosphonate use has several limitations. Those limitations include the high cost of the agents and the need for return trips to the clinic for intravenous treatment. Moreover, many uncertainties surround bisphosphonate use-for example, the timing of bisphosphonate initiation, the choice of bisphosphonate to use, the optimal duration of treatment, and the appropriate means to identify patients who will and will not benefit. In addition, potentially serious adverse effects have been associated with bisphosphonate use-for example, renal toxicity, gastrointestinal side effects, and osteonecrosis of the jaw. The present review is intended as a primer for oncology specialists who treat patients with bone metastases secondary to breast cancer. It focuses on bisphosphonate treatment guidelines, the evidence for those guidelines, and a discussion of new therapeutic agents. It also discusses the use of biochemical markers of bone metabolism, which show promise for predicting the risk of a patient's developing a SRE and of benefiting from bisphosphonate treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".