Bone Health and Endocrine Therapy with Ovarian Function Suppression in Premenopausal Early Breast Cancer: A Real-Life Monocenter Experience with Denosumab
Bibliographic record
Abstract
Adjuvant endocrine therapy for early breast cancer significantly reduces recurrence but increases bone fragility. Given limited data on denosumab (60 mg every 6 months) in premenopausal patients receiving endocrine therapy for early breast cancer, we conducted a retrospective real-world study at the Gemelli Hospital (September 2018-January 2025). A descriptive analysis was performed. The primary endpoint was to assess efficacy, evaluated by changes in bone mineral density via dual-energy X-ray absorptiometry and by monitoring bone turnover markers, particularly serum C-terminal telopeptide of type I collagen. Safety was evaluated based on adverse endocrine therapy events (osteoporotic fractures) and adverse denosumab events (osteonecrosis of the jaw). Sixty-nine patients were eligible for the study. Endocrine therapy included ovarian function suppression with exemestane (89.8%) or tamoxifen (10.1%). Baseline spinal osteoporosis decreased from 20.3% to 5.8%, osteopenia from 39.1% to 34.8%, with normal T-scores rising from 17.4% to 34.8%. Femoral improvements were similar. Serum C-terminal telopeptide of type I collagen levels (evaluated in 35.8%) showed stable reduction in 97%. Denosumab adherence was 89.9%. One osteonecrosis of the jaw case occurred (1.4%); no fractures were reported. Denosumab demonstrated efficacy in improving bone density and reducing bone turnover, with excellent adherence and favorable safety. Longer follow-up is needed to assess post-discontinuation effects.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".