Prevalence of cognitive frailty and associations with other frailty domains in a Spanish community‐dwelling sample
Bibliographic record
Abstract
Abstract Background The concept of cognitive frailty has been defined as the simultaneous presence of frailty and mild cognitive impairment in absence of dementia, although recent approaches has considered cognitive frailty as a distinct construct and has taken into account different domains of physical frailty (De Roeck et al., 2020). The aim of the present study is to analyze the prevalence of cognitive frailty in groups with different degrees of cognitive impairment. Method A total of 285 community‐dwelling middle‐age and older adults, aged >50 years, from Galicia and Valencia (northwest and southeast of Spain respectively) participated. Cognitive function was assessed using MoCA test, and cognitive complaints with the Spanish version of the Everyday Memory Questionnaire. Participants were classified as objective cognitively impaired, cognitively unimpaired, and cognitively unimpaired with subjective cognitive complaints. Physical frailty criteria included: a) unintentional weight loss or lack of appetite in the last three months; b) self‐reported exhaustion, measured with the General Health Questionnaire‐12 question about affective state; c) weakness, measured with a dynamometer to determine handgrip strength; d) slow walking speed, measured through a timed‐up and go task; and e) low physical activity, measured with the reduced Spanish version of the Minnesota Leisure Time Physical Activity Questionnaire (VREM). Participants were classified as robust if they met none of the criteria, pre‐frail if they met 1 or 2 criteria, and frail if they met 3 or more criteria, corrected by gender and age. Results The prevalence of frailty was 2.5% in the cognitively unimpaired group, 12.2% in the group with cognitive complaints, and 9.6% in the group with objective cognitive impairment (chi‐square=18.37;p<0.01) (Table 1). The co‐occurrence with physical frailty is more frequent with slow walking speed and, in the group with complaints, with self‐reported exhaustion. Conclusion The prevalence of physical frailty was higher in both cognitive complaints and cognitive impairment groups, compared with the cognitively unimpaired group. The subjective cognitive complaints can play a relevant role in characterizing the relation between physical and cognitive impairments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".