Effect of denosumab versus zoledronic acid in preventing skeletal-related events in patients with bone metastases by baseline characteristics
Bibliographic record
Abstract
BACKGROUND: Analyses of phase III trials showed that denosumab was superior to zoledronic acid (ZA) in preventing skeletal-related events (SREs) irrespective of age, history of SREs, or baseline pain status. This analysis assessed the risk of SREs across additional baseline characteristics. PATIENTS AND METHODS: Patients (N = 5543) from three phase III trials who had breast cancer, prostate cancer, or other solid tumours and one or more bone metastasis were included. Superiority of denosumab versus ZA in reducing risk of first SRE and first and subsequent SREs was assessed in subgroups defined by the Eastern Cooperative Oncology Group performance status (ECOG PS), bone metastasis location, bone metastasis number, visceral metastasis presence/absence, and urinary N-telopeptide (uNTx) level using Cox proportional hazards and Anderson-Gill models. Subgroups except bone metastasis location were also assessed for each solid tumour type. RESULTS: Compared with ZA, denosumab significantly reduced the risk of first SRE across all subgroups (hazard ratio [HR] ranges: ECOG PS, 0.79-0.84; bone metastasis location, 0.78-0.83; bone metastasis number, 0.78-0.84; visceral metastasis presence/absence, 0.80-0.82; uNTx level, 0.73-0.86) and reduced the risk of first and subsequent SREs in all subgroups (HR ranges: ECOG PS, 0.76-0.83; bone metastasis location, 0.78-0.84; bone metastasis number, 0.79-0.81; visceral metastasis presence/absence, 0.79-0.81; uNTx level, 0.74-0.83). Similar results were observed in subgroups across tumour types. CONCLUSION: Denosumab was superior to ZA in preventing SREs in patients with bone metastases from advanced cancer, regardless of ECOG PS, bone metastasis number, baseline visceral metastasis presence/absence, and uNTx level.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.000 | 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".