A systematic review of dosing frequency with bone-targeted agents for patients with bone metastases from breast cancer
Bibliographic record
Abstract
BACKGROUND: Bone-targeted agents are usually administered to breast cancer patients with bone metastases every 3-4 weeks. Less frequent ('de-escalated') treatment may provide similar benefits with improved safety and reduced cost. METHODS: To systematically review randomised trials comparing de-escalated treatment with bone-targeted agents (i.e. every 12-16 weeks) to standard treatment (i.e. every 3-4 weeks), a formal systematic review of the literature was performed. Two individuals independently screened citations and full text articles. Random effects meta-analyses of clinically important outcomes were planned provided homogeneous studies were identified. RESULTS: Five relevant studies (n=1287 patients) were identified. Sample size ranged from 38 to 425. Information on outcomes including occurrence of SREs, bone pain, urinary N-telopeptide concentrations, serum C-telopeptide concentrations, pain medication use and safety outcomes was not consistently available. Two trials were non-inferiority studies, two dose-response evaluations and one was a pilot study. Bone-targeted agents use varied between studies, as did duration of prior therapy. Patient populations were considered heterogeneous in several ways, and thus no meta-analyses were performed. Observations from the included studies suggest there is potential that 3 month de-escalated treatment may provide similar benefits compared to 3-4 weekly treatment and that lower doses of zoledronic acid and denosumab might be equally effective. CONCLUSIONS: Studies comparing standard and de-escalated treatment with bone-targeted agents in breast cancer are rare. The benefits of standard treatment compared to de-escalated therapy on important clinical outcomes remain unclear. Future pragmatic studies must be conducted to determine the merits of this approach.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".