Validity of handgrip strength for assessing cognition and psychotic symptoms in hospitalized patients with stable schizophrenia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".