ASSOCIATIONS OF PERCEIVED PHYSICAL AND MENTAL FATIGABILITY WITH COGNITIVE PERFORMANCE
Bibliographic record
Abstract
Abstract Greater perceived fatigability has been associated with neurological diseases, but we do not know whether there are associations with cognition among at-risk older adults. At baseline, SOMMA participants completed the Pittsburgh Fatigability Scale (PFS) Physical and Mental subscales (each range 0–50; higher scores = greater fatigability; clinically meaningful increment=4-point physical, 3-point mental) and four cognitive function assessments [Digit Symbol Substitution Test (DSST), Montreal Cognitive Assessment (MoCA), Trail Making Test Part B (TMT-B), and California Verbal Learning Test-Second Edition, Short Form (CVLT-II SF)]. Linear regression quantified associations between PFS subscales and cognitive assessment scores adjusting for site, age, sex, race, education, marital status, and history of stroke, cancer, heart failure, and lung disease. In the 873 participants (59.2% women; age 76.3+/-5.0 years; 85% White), 54% had greater physical fatigability (PFS Physical≥15) and 23% had greater mental fatigability (PFS Mental≥13). Prevalence of cognitive impairment was 2% moderate (MoCA 10-17) and 0% severe ( <10). After adjustments, for each 4-point higher PFS Physical score participants had 0.8 fewer correct DSST items [Beta coefficient and 95% confidence interval: -0.8 (-1.2, -0.4); n=866] and 2.0 seconds slower TMT-B time [2.0 (0.1, 3.8); n=835]. Associations were similar for each 3-point higher PFS Mental score [DSST: -0.7 (-1.1, -0.4) and TMT-B time: 2.5 (1.0, 4.1)]. Neither PFS subscale was associated with MoCA or CVLT. Our results suggest that higher perceived physical and mental fatigability scores may both be indicative of cognitive impairment, particularly in processing speed.
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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 | 0.007 |
| 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) | 0.003 | 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".