The Global Longitudinal Study of Osteoporosis in Women (GLOW): rationale and study design
Bibliographic record
Abstract
SUMMARY: The Global Longitudinal study of Osteoporosis in Women (GLOW) is a prospective cohort study involving 723 physicians and 60,393 women subjects >or=55 years. The data will provide insights into the management of fracture risk in older women over 5 years, patient experience with prevention and treatment, and distribution of risk among older women on an international basis. INTRODUCTION: Data from cohort studies describing the distribution of osteoporosis-related fractures and risk factors are not directly comparable and do not compare regional differences in patterns of patient management and fracture outcomes. METHODS: The GLOW is a prospective, multinational, observational cohort study. Practices typical of each region were identified through primary care networks organized for administrative, research, or educational purposes. Noninstitutionalized patients visiting each practice within the previous 2 years were eligible. Self-administered questionnaires were mailed, with 2:1 oversampling of women >or=65 years. Follow-up questionnaires will be sent at 12-month intervals for 5 years. RESULTS: A total of 723 physicians at 17 sites in ten countries agreed to participate. Baseline surveys were mailed (October 2006 to February 2008) to 140,416 subjects. After the exclusion of 3,265 women who were ineligible or had died, 60,393 agreed to participate. CONCLUSIONS: GLOW will provide contemporary information on patterns of management of fracture risk in older women over a 5-year period. The collection of data in a similar manner in ten countries will permit comparisons of patient experience with prevention and treatment and provide insights into the distribution of risk among older women on an international basis.
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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 | 0.087 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".