Patterns of adjuvant bone modifying agent use in patients with early-stage breast cancer in the United States
Bibliographic record
Abstract
PURPOSE: Based on improved survival, the 2017 ASCO and Cancer Care Ontario clinical guidelines (ACGD) recommended consideration of adjuvant bisphosphonates for postmenopausal women with early-stage breast cancer (EBC). However, small survey-based studies suggest inconsistent prescribing. This study evaluated receipt of adjuvant bone modifying agents (BMAs) in the United States before and after publication of the 2017 ACGD. METHODS: This nationwide retrospective cohort study used a deidentified electronic health record-derived database to identify patients diagnosed with stage I-III EBC treated at health care practices from 2012 to 2019. We defined adjuvant BMA (bisphosphonates or denosumab) use as first dose received within 24 months of EBC diagnosis. We used Chi-squared and multivariable logistic regression analyses to compare the proportion of patients receiving adjuvant BMAs pre- and post-ACGD and identify factors associated with receipt of any BMA as well as bisphosphonates alone. RESULTS: Our cohort included 11,470 patients. Most patients were 50 years of age or older (82%), and had stage I (57%), node-negative (70%) and estrogen receptor (ER)-positive (76%) breast cancer. Patients diagnosed post-ACGD (2017-19) were more likely to receive adjuvant BMAs (9%) than patients diagnosed in earlier years (7.4%; odds ratio [OR] 1.23; 95% confidence interval (CI) 1.08-1.42; p = 0.002). Post-menopausal status, age ≥ 50, receipt of adjuvant chemotherapy and endocrine therapy, and coexisting bone loss diagnoses were significantly associated with increased receipt of adjuvant BMAs. Among BMA recipients, 65.8% received denosumab only, 32.6% received bisphosphonates only, and 1.4% received both. CONCLUSIONS: Even after release of the ACGD guidelines, adjuvant BMA prescribing was low, and the majority of patients who received BMA did not receive bisphosphonates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".