Bone strength and management of postmenopausal fracture risk with antiresorptive therapies: considerations for women’s health practice
Bibliographic record
Abstract
Bone strength - and, hence, fracture risk - reflects the structural and material properties of the skeleton, which changes with bone turnover during aging and following effective pharmacotherapy. A variety of powerful new techniques (quantitative computed tomography, as well as peripheral quantitative computed tomography and high-resolution peripheral quantitative computed tomography) provide precise images of bone structure and can be used to model the response of specific bones to different types of mechanical load. This review explores the various components of bone strength and the clinical significance of measures, such as bone mineral density, bone turnover markers, and modern imaging data, with regard to fracture risk in women with postmenopausal osteoporosis, before and after initiating antiresorptive therapy. These imaging and related techniques offer an ever-clearer picture of the changes in bone structure and bone mineral metabolism during normal aging and in osteoporosis, as well as in response to treatment. However, because the newer techniques are not yet available in routine practice, validated tools for absolute fracture risk assessment remain essential for clinical decision making. These tools, which are tailored to patient risk data in individual countries, are based on bone mineral density and other readily available clinical data. In addition, bone turnover marker measurements can be useful in assessing risk and guiding treatment decisions for women with postmenopausal osteoporosis. Such tests may be used before starting a patient on antiresorptive therapy and for ongoing monitoring of treatment effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".