Changes in Bone Turnover Marker Levels and Clinical Outcomes in Patients with Advanced Cancer and Bone Metastases Treated with Bone Antiresorptive Agents
Bibliographic record
Abstract
PURPOSE: Bone antiresorptive agents can significantly reduce bone turnover markers (BTM) in patients with advanced cancer. We evaluated association of changes in BTMs with overall survival (OS), disease progression (DP), and disease progression in bone (DPB) in patients with advanced cancer and bone metastases following denosumab or zoledronic acid treatment. EXPERIMENTAL DESIGN: This is an integrated analysis of patient-level data from three identically designed, blinded, phase III trials with patients randomized to subcutaneous denosumab or intravenous zoledronic acid. Levels of the BTMs urinary N-telopeptide (uNTx) and serum bone-specific alkaline phosphatase (sBSAP) measured at study entry and month 3 were analyzed. OS, DP, and DPB were compared in patients with BTMs ≥ median versus < median based on month 3 assessments. RESULTS: uNTx levels ≥ the median of 10.0 nmol/mmol at month 3 were associated with significantly reduced OS compared with levels < median (HR for death, 1.85; P < 0.0001). sBSAP levels ≥ median of 12.6 ng/mL were associated with significantly reduced OS compared with levels < median (HR, 2.44; P < 0.0001). uNTx and sBSAP levels ≥ median at month 3 were associated with significantly greater risk of DP (HR, 1.31; P < 0.0001 and HR, 1.71; P < 0.0001, respectively) and DPB (HR, 1.11; P = 0.0407 and HR, 1.27; P < 0.0001, respectively). CONCLUSIONS: BTM levels ≥ median after 3 months of bone antiresorptive treatment were associated with reduced OS and increased risk of DP and DPB. Assessment of uNTx and sBSAP levels after bone antiresorptive therapy may add to identification of patients at risk for worse clinical outcomes. Clin Cancer Res; 22(23); 5713-21. ©2016 AACR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".