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Record W2611087240 · doi:10.1080/13607863.2017.1320700

Everyday cognitive functioning and global cognitive performance are differentially associated with physical frailty and chronological age in older Chinese men and women

2017· article· en· W2611087240 on OpenAlexaboutno aff
Tianyin Liu, Gloria Hoi Yan Wong, Hao Luo, Jennifer YM Tang, Jiaqi Xu, Jacky Chak Pui Choy, Terry YS Lum

Bibliographic record

VenueAging & Mental Health · 2017
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
FundersUniversity of Hong Kong
KeywordsCognitionPsychologyGerontologyCognitive declineDevelopmental psychologyDementiaMedicineDisease

Abstract

fetched live from OpenAlex

OBJECTIVES: Intact cognition is a key determinant of quality of life. Here, we investigated the relative contribution of age and physical frailty to global and everyday cognition in older adults. METHODS: Data came from 1396 community-dwelling, healthy Chinese older adults aged 65 or above. We measured their global cognition using the Cantonese Chinese Montreal Cognitive Assessment, everyday cognition with the short Chinese Lawton Instrumental Activities Daily Living scale, and physical frailty using the Fatigue, Resistance, Ambulation, Illness, and Loss of Weight Scale and grip strength. Multiple regression analysis was used to evaluate the comparative roles of age and physical frailty. RESULTS: In the global cognition model, age explained 12% and physical frailty explained 8% of the unique variance. This pattern was only evident in women, while the reverse (physical frailty explains a greater extent of variance) was evident in men. In the everyday cognition model, physical frailty explained 18% and chronological age explained 9% of the unique variance, with similar results across both genders. CONCLUSION: Physical frailty is a stronger indicator than age for everyday cognition in both genders and for global cognition in men. Our findings suggest that there are alternative indexes of cognitive aging than chronological age.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.315
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2017
Admission routes1
Has abstractyes

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