Skeletal health in postmenopausal survivors of early breast cancer
Bibliographic record
Abstract
Estrogen plays an important role in the skeletal health of all women. Many therapies used in the treatment of breast cancer reduce estrogen levels and have the potential to affect bone negatively by increasing the risk of osteoporosis and associated bone fractures. The long-term effects of systemic endocrine therapy on bone, therefore, are an important consideration in the adjuvant setting. Tamoxifen has been shown to have a moderate protective effect on postmenopausal bone due to its partial estrogen agonist activity; however, its long-term use is potentially associated with negative side effects, such as an increased risk of thromboembolic disease and endometrial cancer. Newer agents, the third-generation aromatase inhibitors (AIs), anastrozole, letrozole and exemestane, for example, do not possess estrogen agonist effects and have improved breast cancer outcomes when compared to the standard 5 years of tamoxifen. However, patients treated with adjuvant AIs have been shown to have an increased incidence of osteoporosis and osteoporotic fractures. In order to select the optimal adjuvant therapy for each patient, it is important to assess the overall risk:benefit ratio for each endocrine strategy. All postmenopausal women should follow published guidelines to assess the risk of osteoporosis and, where appropriate, they should receive bone mineral density monitoring. Postmenopausal women with breast cancer who are at increased risk of osteoporotic fracture should be identified and managed with appropriate nonpharmacologic and pharmacologic measures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".