Assessment of the benefits of bone modifying agents in the management of advanced breast, prostate, and lung cancers
Bibliographic record
Abstract
PURPOSE OF REVIEW: Skeletal metastases occur in approximately 80% of advanced breast, 70% of advanced prostate, and 30% of lung cancers, and place patients at increased risk of skeletal related events (SRE). Bone modifying agents (BMAs) have been shown to prevent or delay SRE development. Our objective was to summarize the role of these agents in the management of these three cancers. RECENT FINDINGS: Total 52 studies met our inclusion criteria. These highlighted the benefit of BMAs in reducing SREs in metastatic breast and castrate resistant prostate cancer (mCRPC), with less clear impact on reducing SRE in lung cancer, or on improving progression-free and overall survival due to significant heterogeneity in trial design and outcomes. Benefits in SRE reduction occurred with bisphosphonates and denosumab, however when compared, denosumab was superior. Denosumab however is not more cost effective, and multiple trials support potential de-escalation to either 12 weekly dosing or other reduced duration. SUMMARY: There is a large body of evidence to support the role of BMAs in reducing SREs in metastatic breast and mCRPC. Impact on survival outcomes is heterogeneous, and future large database trials would be helpful in identifying which subgroups of patients truly have survival benefit from BMAs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.005 | 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".