Raloxifene use in clinical practice
Bibliographic record
Abstract
OBJECTIVE AND METHODS: In this article, we provide an interdisciplinary concise review of the effects of raloxifene on breast, bone, and reproductive organs, as well as the adverse events that may be associated with its use. RESULTS: Raloxifene has been shown to prevent osteoporosis in postmenopausal women (PMW) with low bone mass and prevent vertebral fractures in those with osteoporosis/low bone mass; it has not been shown to reduce the risk of nonvertebral fractures. Raloxifene reduces the risk of invasive breast cancer in PMW with osteoporosis or at high risk of breast cancer. The risk of venous thromboembolism has been consistently shown to be increased with raloxifene, so it should not be used in women at high risk of venous thromboembolism. Although raloxifene does not increase, nor decrease, the risk of coronary or stroke events overall, in the raloxifene trial of PMW at increased risk of coronary events, the incidence of fatal stroke was higher in women assigned raloxifene versus placebo. CONCLUSIONS: Based on its approved indications, it is appropriate to prescribe raloxifene to prevent or treat osteoporosis, as well as to reduce the risk of invasive breast cancer in PMW with osteoporosis or at high risk of breast cancer. Women at increased risk of both fracture and invasive breast cancer are those most likely to receive a dual benefit with raloxifene. Decision making must involve the incorporation of the woman's personal feelings about the risks and benefits of raloxifene therapy, balanced with her interest in reducing risk of fractures and breast cancer through pharmacological intervention.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".