Hierarchy and Speed of Loss in Physical Functioning: A Comparison Across Older U.S. and English Men and Women
Bibliographic record
Abstract
BACKGROUND: We aimed to identify the hierarchy of rates of decline in 16 physical functioning measures in U.S. and English samples, using a systematic and integrative coordinated data analysis approach. METHODS: The U.S. sample consisted of 13,612 Health and Retirement Study participants, and the English sample consisted of 5,301 English Longitudinal Study of Ageing participants. Functional loss was ascertained using self-reported difficulties performing 6 activities of daily living and 10 mobility tasks. The variables were standardized, rates of decline were computed, and mean rates of decline were ranked. Mann-Whitney U tests were performed to compare rates of decline between studies. RESULTS: In both studies, the rates of decline followed a similar pattern; difficulty with eating was the activity that showed the slowest decline and climbing several flights of stairs and stooping, kneeling, or crouching the fastest declines. There were statistical differences in the speed of decline in all 16 measures between countries. American women had steeper declines in 10 of the measures than English women. Similar differences were found between American and English men. CONCLUSIONS: Reporting difficulties climbing several flights of stairs without resting, and stooping, kneeling, or crouching are the first indicators of functional loss reported in both populations.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".