Use of bone-modifying agents among breast cancer patients with bone metastasis: evidence from oncology practices in the US
Bibliographic record
Abstract
PURPOSE: Bone-modifying agents (BMAs) are recommended for women with bone metastasis from breast cancer to prevent skeletal-related events. We examined the usage patterns and identified the factors associated with the use of BMAs (denosumab and intravenous bisphosphonates) among women in the US. PATIENTS AND METHODS: Electronic health records from oncology clinics were used to identify women diagnosed with bone metastasis from breast cancer between 2013 and 2014. Patients were excluded if they had recently used a BMA or had concurrent cancer at an additional primary site. The incidence of BMA initiation, interruption, and reinitiation were estimated using competing risk regression models. A generalized linear model was used to estimate risk factors for treatment initiation and interruption. RESULTS: There were 589 women diagnosed with bone metastasis from breast cancer. By 1 year, 68% of these patients (95% CI: 64%, 71%) had initiated treatment with a BMA. Denosumab and zoledronic acid were the most commonly used agents, whereas pamidronate was used infrequently. Young women were more likely to initiate a BMA than older women (adjusted risk difference: 6.4 [95% CI: 1.5, 10.9]). Of the 412 patients who initiated a BMA, 46% (95% CI: 41%, 51%) experienced an interruption within 1 year. Seventy-four percent (95% CI: 68%, 79%) of patients who interrupted their treatment had reinitiated therapy within 1 year of interruption. CONCLUSION: The majority of women diagnosed with bone metastasis from breast cancer initiate a BMA within 1 year of diagnosis, but a large proportion, particularly among the elderly, do not use these therapies.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".