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Record W4307386695 · doi:10.1093/ageing/afac218.278

317 PREVALENCE OF MOTORIC COGNITIVE RISK SYNDROME IN PATIENTS WITH MILD COGNITIVE IMPAIRMENT ATTENDING A MEMORY CLINIC

2022· article· en· W4307386695 on OpenAlexaboutno aff
Gillian Harte, Seán Kennelly, Tim Dukelow

Bibliographic record

VenueAge and Ageing · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDementiaGaitCognitionMemory clinicCognitive impairmentPhysical therapyMontreal Cognitive AssessmentPsychological interventionInclusion and exclusion criteriaPhysical medicine and rehabilitationPediatricsPsychiatryDiseaseInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

Abstract Background Mild Cognitive Impairment (MCI) alone is not predictive of progression to dementia. However, previous research has found that the combination of MCI and slow gait speed, termed Motoric Cognitive Risk syndrome (MCR), is associated with a three-fold increased risk for the development of dementia. This study aimed to identify the prevalence of MCR among patients with MCI attending a memory clinic at a large teaching hospital. Methods: Participants Participants were recruited from the memory clinic at the study site. All participants matching the inclusion criteria were invited to participate in the study. Inclusion criteria: Age > 55 years, independently mobile > 50m unaided, diagnosis of MCI. All participants gave informed consent prior to testing. Procedure Gait speed (in cm/s) during normal pace walking was measured using a 5-metre computerised walkway with embedded sensors (GaitRite). Data Analysis Gait speed data was dichotomised into “normal” or “low” using previously defined gait speed cut-offs derived from age-based norms. Data was analysed using descriptive statistics on Microsoft Excel. Results 31 participants completed testing (Males: 12; Females: 19). Mean age was 73.65 (range 63-84). Median score on the Montreal Cognitive Assessment (MoCA) was 20 (range 4-28). N = 2 (6.45%) reached criteria for MCR. Conclusion Although this study had small numbers, prevalence of MCR was similar to that previously reported among similar populations. This approach to identifying those at high risk for dementia may allow clinicians to target and prioritise further investigations and preventative interventions.

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)0.001
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 score0.008
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.285
Teacher spread0.271 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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