Neuroimaging signatures of frailty: A population‐based study in community‐dwelling older adults (the <scp>A</scp>tahualpa <scp>P</scp>roject)
Bibliographic record
Abstract
AIMS: Frailty is a geriatric state of physical vulnerability that might be associated with cognitive decline in the absence of a concurrent neurodegenerative disorder. This assumes that neuroimaging studies are normal, but such examinations have rarely been considered for a frailty work-up. The present study identifies neuroimaging signatures in older adults interviewed with the Edmonton Frail Scale (EFS). METHODS: Community-dwellers aged ≥60 years enrolled in the Atahualpa Project were invited to undergo brain magnetic resonance imaging. Using generalized regression models, we evaluated the association between frailty and diffuse cortical and subcortical brain damage, after adjusting for relevant confounders. Multivariate models estimated the interaction of age in the association between frailty and these neuroimaging signatures. RESULTS: Out of 298 participants (mean age 70 ± 8 years, 57% women), 151 (51%) had moderate-to-severe cortical atrophy and 74 (25%) had moderate-to-severe white matter hyperintensities of presumed vascular origin. Mean EFS scores were 5 ± 3 points, with 140 (47%) individuals classified as robust, 65 (22%) as pre-frail and 93 (31%) as frail. Multivariate models showed a significant association between cortical atrophy with the continuous (P = 0.002) and the categorized (P = 0.008) EFS score. The relationship between white matter hyperintensities and the EFS was marginal. According to interaction models, prefrail or frail individuals aged ≥67 years presented more prominent neuroimaging signatures of diffuse cortical or subcortical damage than their robust counterparts. CONCLUSIONS: Neuroimaging signatures of frailty are mainly related to age. This reinforces the importance of early frailty detection to reduce its catastrophic consequences. Geriatr Gerontol Int 2017; 17: 270-276.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".