Patterns of Anti-Osteoporosis Medication Use among Women at High Risk of Fracture: Findings from the Global Longitudinal Study of Osteoporosis in Women (GLOW)
Bibliographic record
Abstract
OBJECTIVE: To assess patterns of anti-osteoporosis medication (AOM) use over 3 years among women at high risk of major fracture. METHODS: The GLOW registry follows a cohort of more than 40,000 women aged ≥ 55 from 615 primary care practices in 10 countries. Self-administered surveys (baseline, 12, 24, and 36 months) collected data on patient characteristics, perception of fracture risk, and AOM use. FRAX scores were calculated from the baseline surveys and women classified as high risk if their FRAX 10-year probability of major fracture was ≥ 20%. RESULTS: A total of 5774 women were classified as at high risk and had complete data over 3 years. At baseline, 2271 (39%) reported receiving AOM, 739 (13%) reported prior but not current use, and 2764 (48%) said they had never used AOM. Over 3 years, 85% of baseline non-users continued as non-users and 15% initiated AOM; among baseline users, 49% continued the same medication class, 29% stopped AOM, and 12% switched. Women who stopped AOM were less likely to self-report osteoporosis (HR 0.56, 95% CI 0.42-0.75) than women who continued AOM. Compared with non-users who did not begin treatment, women initiating AOM were more likely to report a diagnosis of osteoporosis (HR 11.3, 95% CI 8.2-15.5) or osteopenia (HR 4.1, 95% CI 2.9-5.7) and be very concerned about osteoporosis (HR 1.9, 95% CI 1.3-2.8). CONCLUSIONS: Less than 40% of women at high risk of fracture reported taking AOM. Women who stopped AOM were less likely to believe they have osteoporosis. Women who initiated treatment appeared motivated primarily by a diagnosis of osteoporosis or osteopenia and concern about the condition.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".