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Record W2953469495 · doi:10.31014/aior.1994.02.02.42

Weight Changes and Cognitive Functions in Patients with Stroke: Case Report

2019· article· en· W2953469495 on OpenAlexaboutno aff
Walaa M. Ragab

Bibliographic record

VenueJournal of Health and Medical Sciences · 2019
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentCognitionCorrelationBody mass indexStroke (engine)Positive correlationMedicineNegative correlationRehabilitationAffect (linguistics)Internal medicinePhysical therapyPsychologyCognitive impairmentPsychiatry

Abstract

fetched live from OpenAlex

Objective of the study: Cognition is an important factor for determining the rate of recovery of stoke so finding the factors that might affect cognition is important to improve it and so to improve recovery rate in patients with stroke. Methodology: Fifteen chronic stroke male patients were recruited to this study. The patients age ranged from55 to 65. All patients were assessed for body mass index (BMI) and also for cognitive functions by Montreal Cognitive Assessment (MOCA) scale and rehacom. Results: the study found a Negative strong correlation between MOCA and BMI (R= -.95),Negative moderate correlation between BMI and Attention( R=-.66) ,Weak negative correlation between BMI and memory ( R=-.38),Weak positive correlation between MOCA and memory (R=.38),Moderate positive correlation between MOCA and attention (R=.61) and Strong positive correlation between memory and solutions (R=.77).Conclusion: There is a negative correlation between BMI and cognition, so it should consider body weight management in the rehabilitation of stroke patients to improve cognitive functions.

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)0.001
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 score0.053
Threshold uncertainty score0.112

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.019
GPT teacher head0.326
Teacher spread0.307 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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