Pain outcomes in patients with advanced breast cancer and bone metastases
Bibliographic record
Abstract
BACKGROUND: In this study, the authors evaluated the effect of denosumab versus zoledronic acid (ZA) on pain in patients with advanced breast cancer and bone metastases. METHODS: The prevention of pain, reduction in pain interference with daily life activities, and the proportion of patients requiring strong opioid analgesics were assessed in a randomized, double-blind, double-dummy phase 3 study comparing denosumab with ZA for preventing skeletal-related events in 2046 patients who had breast cancer and bone metastases. Patients completed the Brief Pain Inventory-Short Form at baseline and monthly thereafter. RESULTS: Fewer patients who received denosumab reported a clinically meaningful worsening of pain severity (≥2-point increase) from baseline compared with patients who received ZA, and a trend was observed toward delayed time to pain worsening with denosumab versus ZA (denosumab, 8.5 months; ZA, 7.4 months; P = .08). In patients who had no/mild pain at baseline, a 4-month delay in progression to moderate/severe pain was observed with denosumab compared with ZA (9.7 months vs 5.8 months; P = .002). Denosumab delayed the time to increased pain interference by approximately 1 month compared with ZA (denosumab, 16.0 months; ZA, 14.9 months; P = .09). The time to pain improvement (P = .72) and the time to decreased pain interference (P = .92) were similar between the groups. Fewer denosumab-treated patients reported increased analgesic use from no/low use at baseline to strong opioid use. CONCLUSIONS: Denosumab demonstrated improved pain prevention and comparable pain palliation compared with ZA. In addition, fewer denosumab-treated patients shifted to strong opioid analgesic use.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".