PHARMACOLOGICAL MANAGEMENT OF OSTEOPOROSIS IN POSTMENOPAUSAL WOMEN: THE CURRENT STATE OF THE ART
Bibliographic record
Abstract
Osteoporosis is a common disease that increases fracture risk. Fragility fractures bring heavy consequences in terms of mortality and disability, with burdensome health and social costs. In subjects with clinical bone fragility, the first goal is to identify the secondary forms of osteoporosis, especially in young subjects, in males and in patients who recently experienced a fragility fracture. In addition, before considering any sort of treatment, it is fundamental to check for adequate calcium and vitamin D intake, since their deficiency is the most common reason for drug failure. In the last decade of the 20th century, several molecules have been developed and proved to be effective in achieving the true goal of any antiosteoporotic drug: fracture prevention. In this article, we considered the most commonly prescribed antiresorptive drugs (hormonal therapy, bisphosphonates, and denosumab), the anabolic agents (teriparatide), the dual-action drugs (romosozumab), and the drugs characterized by an unclear mechanism of action (strontium ranelate) to provide physicians with useful insights for their clinical practice. We discussed the main criteria for the appropriate choice selection and management of each treatment. Finally, we addressed the current controversies related to treatment discontinuation, sequential, and combination therapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".