Asia–pacific consensus on osteoporotic fracture prevention in postmenopausal women with low bone mass or osteoporosis but no fragility fractures
Bibliographic record
Abstract
Postmenopausal women are at significant risk for osteoporotic fractures due to their rapid bone loss. Half of all postmenopausal women will get an osteoporosis-related fracture over their lifetime, with 25% developing a spine deformity and 15% developing a hip fracture. By 2050, more than half of all osteoporotic fractures will occur in Asia, with postmenopausal women being the most susceptible. Early management can halt or even reverse the progression of osteoporosis. Consequently, on October 31, 2020, the Taiwanese Osteoporosis Association hosted the Asia-Pacific (AP) Postmenopausal Osteoporotic Fracture Prevention (POFP) consensus meeting, which was supported by the Asian Federation of Osteoporosis Societies (AFOS) and the Asia Pacific Osteoporosis Foundation (APOF). International and domestic experts developed ten applicable statements for the prevention of osteoporotic fractures in postmenopausal women with low bone mass or osteoporosis but no fragility fractures in the AP region. The experts advocated, for example, that postmenopausal women with a high fracture risk be reimbursed for pharmaceutical therapy to prevent osteoporotic fractures. More clinical experience and data are required to modify intervention tactics.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".