How regional versus global thresholds for physical activity and grip strength influence physical frailty prevalence and mortality estimates in PURE: a prospective multinational cohort study of community-dwelling adults
Bibliographic record
Abstract
Objectives Handgrip strength and physical activity are commonly used to evaluate physical frailty; however, their distribution varies worldwide. The thresholds that identify frail individuals have been established in high-income countries but not in low-income and middle-income countries. We created two adaptations of physical frailty to study how global versus regional thresholds for handgrip strength and physical activity affect frailty prevalence and its association with mortality in a multinational population. Design, setting and participants Our sample included 137 499 adults aged 35–70 years (median age: 61 years, 60% women) from Population Urban Rural Epidemiology Studies community-dwelling prospective cohort across 25 countries, covering the following geographical regions: China, South Asia, Southeast Asia, Africa, Russia and Central Asia, North America/Europe, Middle East and South America. Primary and secondary outcome measures We measured and compared frailty prevalence and time to all-cause mortality for two adaptations of frailty. Results Overall frailty prevalence was 5.6% using global frailty and 5.8% using regional frailty . Global frailty prevalence ranged from 2.4% (North America/Europe) to 20.1% (Africa), while regional frailty ranged from 4.1% (Russia/Central Asia) to 8.8% (Middle East). The HRs for all-cause mortality (median follow-up of 9 years) were 2.42 (95% CI: 2.25 to 2.60) and 1.91 (95% CI: 1.77 to 2.06) using global frailty and regional frailty, respectively, (adjusted for age, sex, education, smoking status, alcohol consumption and morbidity count). Receiver operating characteristic curves for all-cause mortality were generated for both frailty adaptations. Global frailty yielded an area under the curve of 0.600 (95% CI: 0.594 to 0.606), compared with 0.5933 (95% CI: 0.587 to 5.99) for regional frailty (p=0.0007). Conclusions Global frailty leads to higher regional variations in estimated frailty prevalence and stronger associations with mortality, as compared with regional frailty. However, both frailty adaptations in isolation are limited in their ability to discriminate between those who will die during 9 years’ follow-up from those who do not.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".