THE ASSOCIATION BETWEEN FRAILTY AND PERCEIVED FATIGABILITY IN THE LONG LIFE FAMILY STUDY
Bibliographic record
Abstract
Abstract Higher levels of frailty, quantified by a frailty index (FI), may be linked to fatigue severity as tasks become more physically and mentally demanding. However, the association between frailty and fatigability—quantification of vulnerability to fatigue in relation to specific intensity and duration of activities—has not been assessed. Using cross-sectional data from the Long Life Family Study Visit 2 (2014–2017; n=2,524; mean age +/- standard deviation 71.4+/-11.2 years; 55% women; 99% White), we examined the association between a 79-item FI (ratio of number of health problems reported (numerator) out of the 79 (denominator); higher percentage=greater frailty) and perceived physical and mental fatigability using the Pittsburgh Fatigability Scale (PFS) (range 0–50; higher scores=greater fatigability). Mean+/-SD FI scores were 0.08+/-0.06 and mean+/-SD PFS Physical and Mental scores were 13.7+/-9.6 (39.5% more severe, >=15) and 7.9+/-8.9 (22.8% more severe, >=13), respectively. Both PFS subscale scores were higher for each 0.10 increment in FI. Mean PFS scores were 10.7 and 34.2 (Physical) and 5.7 and 28.8 (Mental) for FI scores of < 0.10 (non-frail) and ≥0.30 (moderate-severely frail), respectively. In mixed effects models, a 0.03 higher FI score (accepted clinically meaningful increase in FI) was associated with 1.9-point higher PFS Physical (95% confidence interval (CI) 1.7–2.1) and 1.7-point higher PFS Mental (95% CI 1.5–1.9) scores after accounting for family structure and adjusting for age, sex, field center, body mass index, smoking status, education, and marital status. Individuals with higher FI scores may benefit from targeted interventions to mitigate further poor health outcomes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".