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Record W4315928836 · doi:10.1002/alz.12905

Dual cognitive and mobility impairments and future dementia ‐ Setting a research agenda

2023· article· en· W4315928836 on OpenAlexaff
Qu Tian, Manuel Montero‐Odasso, Aron S. Buchman, Michelle M. Mielke, Sara Espinoza, Charles DeCarli, Anne B. Newman, Stephen B. Kritchevsky, George W. Rebok, Susan M. Resnick, Madhav Thambisetty, Joe Verghese, Luigi Ferrucci

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsParkwood InstituteLawson Health Research InstituteWestern University
FundersNational Institute on AgingNational Institutes of Health
KeywordsDementiaCognitive declineGerontologyDual (grammatical number)CognitionPsychologyDual roleMedicineNeuroscienceDiseasePathology

Abstract

fetched live from OpenAlex

Dual cognitive and mobility impairments are associated with an increased risk of dementia. Recent studies examining temporal trajectories of mobility and cognitive function in aging found that dual decline is associated with higher dementia risk than memory decline or gait decline only. Although initial data show that individuals with dual decline or impairment have excessive cardiovascular and metabolic risk factors, the causes of dual decline or what underlies dual decline with a high risk of dementia remain largely unknown. In December 2021, the National Institute on Aging Intramural and Extramural Programs jointly organized a workshop on Biology Underlying Moving and Thinking to explore the hypothesis that older persons with dual decline may develop dementia through a specific pathophysiological pathway. The working group discussed assessment methods for dual decline and possible mechanisms connecting dual decline with dementia risk and pinpointed the most critical questions to be addressed from a translational perspective.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.024
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0030.003
Science and technology studies0.0050.005
Scholarly communication0.0100.017
Open science0.0040.008
Research integrity0.0120.012
Insufficient payload (model declined to judge)0.0090.001

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 head0.060
GPT teacher head0.385
Teacher spread0.324 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations40
Published2023
Admission routes1
Has abstractyes

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