Management of aromatase inhibitor-associated bone loss (AIBL) in women with hormone-sensitive breast cancer: An updated joint position statement of the IOF, CABS, ECTS, IEG, ESCEO, IMS, and SIOG
Bibliographic record
Abstract
Background: Women with hormone-responsive breast cancer who receive adjuvant endocrine treatment with aromatase inhibitors (AI) are known to be at higher fracture risk due to a marked increase in bone resorption. In 2017, several interdisciplinary cancer and bone societies involved in the management of women with AI-associated bone loss (AIBL) published a joint position statement comprising evidence-based recommendations and a practical management algorithm for the assessment of fracture risk and optimal treatment of this patient population. Patients and methods: In order to provide updated recommendations that reflect recent advances in the assessment and management of AIBL since publication of the 2017 joint position statement, a systematic literature review was undertaken to identify relevant studies for analysis, including systematic reviews and meta-analyses. Individual trials identified were assessed for their level of evidence based on design, size, follow-up, and evaluation of safety, as well as the impact of bone directed treatments on breast cancer outcomes. Results: New evidence was combined with the existing recommendations to provide an updated joint position statement regarding fracture risk assessment and implementation of bone-directed therapy. Conclusion: Current published literature, including recent clinical trial reports, systematic reviews and meta-analyses, continue to affirm the high risk of fractures in women with breast cancer who are receiving adjuvant AI treatment, a risk which has been observed to increase with the commonly used approach of extended duration AI therapy (>5 years). Risk factors for fracture and risk assessment in this patient population as well as the most suitable treatment modalities have been updated. Finally, the influence of bone protective treatments on breast cancer outcomes such as incidence of bone metastasis and breast cancer related overall survival have been included.
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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 | 0.035 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".