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Record W4313060865 · doi:10.1589/rika.37.551

Relationship between Mild Cognitive Impairment and Physical Activity in Patients with Heart Disease

2022· article· en· W4313060865 on OpenAlexaboutno aff
Yuki Kimura, Hidetaka FURUYA, Ryo EMORI, Hidehiko KASHIWAGI, Hidenori WATANABE

Bibliographic record

VenueRigakuryoho Kagaku · 2022
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive impairmentMontreal Cognitive AssessmentDementiaMedicineCorrelationPhysical activityCoronary heart diseasePhysical therapyInternal medicineGerontologyCognitionDiseaseCardiologyPsychologyPsychiatryMathematics

Abstract

fetched live from OpenAlex

[Purpose] This study researched the relationship between mild cognitive impairment (MCI) and physical activity (PA) in patients with heart disease. [Participants and Methods] Forty-eight patients (mean age 72.6 ± 6.9 years) with heart disease were included in this study. MCI was evaluated using the Japanese version of the Montreal Cognitive Assessment (MoCA-J). PA was evaluated by a Fitbit inspire (Fitbit Inc.) for a week which calculated the average number of steps per day as a variable. Partial correlation analysis adjusted for age and sex was performed to analyze the correlation between the MoCA-J and PA. [Results] The mean score of the MoCA-J was 25 ± 3.6 points and that of PA was 5337 ± 2534 steps/day. The partial correlation analysis revealed a strong correlation between the MoCA-J and PA (r=0.66). [Conclusion] Early detection of MCI is necessary for reducing the risk of dementia and improving PA.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.317
Teacher spread0.293 · 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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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