Regional Differences in Incident Prefrailty and Frailty
Bibliographic record
Abstract
Background and Objectives: The extent to which greater frailty among American compared with European women reflects individual-level characteristics has not been well studied. To test the hypothesis that cardiometabolic conditions and depression and anxiety confound the relationship between region and incident prefrailty and frailty in American compared with European women. Materials and Methods: The Global Longitudinal Study of Osteoporosis in Women (GLOW) is a 5-year observational cohort study of women aged ≥55 years. A total of 19,674 participants from the United States and Europe were nonfrail at baseline and provided information on characteristics, including body mass index, depression and anxiety, and cardiovascular disease. We used multivariable Cox proportional hazards models to examine the relationship between region and incident frailty and prefrailty. Results: Over 40% of respondents became prefrail or frail during follow-up. Adjusting for age, body mass index, depression and anxiety, cardiovascular disease, and other health-related characteristics, European respondents had a decreased risk of developing prefrailty (2-year hazard ratio [HR]: 0.78, 95% confidence interval [CI]: 0.73–0.84; 3-year HR: 0.74, 95% CI: 0.67–0.81) and frailty (2-year HR: 0.65, 95% CI: 0.56–0.76; 3-year HR: 0.82, 95% CI: 0.68–0.99) compared with American respondents. Risk of incident frailty and prefrailty did not vary by region at 5 years of follow-up. Conclusions: Cardiometabolic conditions and depression and anxiety did not account for increased frailty and prefrailty onset among American compared with European women. Differences in smaller regions and environmental characteristics may contribute to frailty and prefrailty.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".