Incidence and consequences of bone metastases in lung cancer patients
Bibliographic record
Abstract
BACKGROUND: Bone metastases (BM) are common in NSCLC patients. Despite some potential positive effects of bone-targeted therapies, their use in NSCLC is infrequent, which may relate to the overall poor prognosis of advanced lung cancer. We reviewed the literature to evaluate the incidence, consequences and use of bone-targeting agents in lung cancer patients with BM in both the trial and non-trial clinical setting. METHODS: Published prospective and retrospective papers investigating lung cancer and BM, in trial and non-trial settings, were identified and are discussed in this review. RESULTS: BM are common in patients with advanced lung cancer and often present symptomatically with pain and skeletal related events (SREs). Patients with high bone turnover marker levels, multiple BM, and history of pathological fractures have shorter overall survival. In randomized studies bone-targeted therapies reduced the risk of SREs and prolonged the time to first SRE. The use of bone-targeted agents may also be associated with a survival benefit. CONCLUSION: BM are a common problem in advanced lung cancer. While the benefits of bone-targeted therapies have been demonstrated, their use is limited in non-trial populations. If better predictive markers of individual risk were available this might increase the appropriate use of bone-targeted agents.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".