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Record W4206043357 · doi:10.1002/alz.055467

Can community‐based cognitive screening identify individuals with MCI associated changes in complex instrumental activities of daily living?

2021· article· en· W4206043357 on OpenAlexaboutno aff
Timothy S. Marks, Gordon Muir Giles, Dorothy Farrar Edwards

Bibliographic record

VenueAlzheimer s & Dementia · 2021
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsActivities of daily livingMontreal Cognitive AssessmentCognitionCognitive impairmentPsychologyDemographicsGerontologyMedicinePsychiatryDemography

Abstract

fetched live from OpenAlex

Abstract Background Unrecognized deficits in complex instrumental activities of daily living, termed “preclinical disability”, have been identified in individuals experiencing mild cognitive impairment (MCI: Fieo & Stern, 2018; Peterson et al., 2017). Screening for difficulties in the performance of cognitively demanding complex IADLs may facilitate earlier identification of people on the threshold of MCI and can facilitate earlier intervention (Rodakowski et al., 2014). We examined the ability of three cognitive screening tests to identify individuals reporting IADL problems on the ADCS‐ADL‐MCI scale. Methods A sample of 87 adults living independently in the community were assessed with the Menu Task, Mini‐Cog, Montreal Cognitive Assessment (MoCA), the ADCS‐ADL, and the 18 and 24 item versions of the ADCS‐MCI‐ADL scale. Established cut‐off scores for each cognitive screen identified impaired and unimpaired groups. Mann‐Whitney U tests compared differences between unimpaired and impaired groups for each cognitive screen on the 18 and 24 item ADCS‐MCI‐ADL composite scores. Between group differences were also examined for each of the ADCS‐MCI‐ADL‐24 individual items. Results Sample demographics are presented in Table 1. Individuals impaired on the Menu Task had significantly lower composite scores on both the ADCS‐MCI‐ADL‐18 and the ADCS‐MCI‐ADL‐24 (p < .01), although no significant composite score differences were found for the Mini‐Cog (p = .061, p = .155) or the MoCA (p = .053, p = .099). Individual ADCS MCI items were also analyzed to explore the types of IADL impairments identified by each of the three cognitive screens scales. Different patterns of IADL impairment emerged for each of the cognitive screening measures. Individuals impaired on the Mini‐Cog had significantly lower ADCS‐ADL composite scores (p < .05). Conclusion Individuals living in the community who score as impaired on the Menu Task demonstrated significantly lower ADCS‐MCI‐ADL‐18 and ADCS‐MCI‐ADL‐24 composite scores, but no significant difference on ADCS‐ADL. Impairment on each of the cognitive screening measures was associated with lower scores on individual ADCS‐ADL‐MCI complex IADL items. Cognitive screening measures that detect early and subtle changes in function may lead to earlier diagnosis and interventions for cognitively complex IADL deficits.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.066
GPT teacher head0.337
Teacher spread0.272 · 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 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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