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Record W4402897671 · doi:10.1371/journal.pone.0308133

Validity of handgrip strength for assessing cognition and psychotic symptoms in hospitalized patients with stable schizophrenia

2024· article· en· W4402897671 on OpenAlexaboutno aff
Jianlin Pu, Binyou Wang, Yilin Wang

Bibliographic record

VenuePLoS ONE · 2024
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
Fundersnot available
KeywordsPositive and Negative Syndrome ScaleSchizophrenia (object-oriented programming)Montreal Cognitive AssessmentMedicineInternal medicineCognitionEffects of sleep deprivation on cognitive performancePsychosisPsychiatryClinical psychologyPhysical therapyCognitive impairment

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: A correlation between low handgrip strength (HGS), HGS asymmetry, and low cognitive performance has been demonstrated. However, it remains unclear whether low HGS is associated with psychotic symptoms and whether HGS asymmetry is associated with cognitive and psychotic symptoms in hospitalized patients with schizophrenia. This study aimed to investigate the validity of HGS as a measure for assessing cognition and psychotic symptoms in hospitalized patients with stable schizophrenia. METHODS: A total of 235 inpatients with stable schizophrenia were recruited between August 1, 2023, and August 31, 2023. The highest HGS values from three tests on the dominant hand were used to determine low HGS (male < 28 kg, female < 18 kg), and HGS asymmetry was identified when the non-dominant HGS/dominant HGS ratio was outside 0.9-1.1. Cognition and psychotic symptoms were assessed using the Chinese Montreal Cognitive Assessment (MoCA-C) and Positive and Negative Syndrome Scale (PANSS). Generalized linear model analyses examined the relationship between HGS and scale scores. RESULTS: Covariate-adjusted generalized linear models confirmed a strong association between low HGS alone and the MoCA-C score (OR = 0.819, 95% CI = 0.710‒0.945, p = 0.006) and PANSS score (OR = 1.113, 95% CI = 1.036‒1.239, p = 0.006). Similarly, the combination of low and asymmetric HGS was strongly associated with both MoCA-C (OR = 0.748, 95% CI = 0.653‒0.857, p<0.001) and PANSS scores (OR = 1.118, 95% CI = 1.032‒1.211, p = 0.006). CONCLUSIONS: The results suggest that hospitalized patients with schizophrenia and low HGS, with or without asymmetry, are likely to have lower MoCA-C scores and higher PANSS scores. Screening stable schizophrenia patients with low HGS, with or without asymmetry, could be a valuable and straightforward approach to identifying those with lower cognition and severe psychotic symptoms.

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)0.002
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.059
GPT teacher head0.313
Teacher spread0.254 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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