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Record W4285740935 · doi:10.2147/ndt.s367392

The Predictive Role of Aberrant Metabolic Parameters and Negative Automatic Thinking on the Cognitive Impairments Among Schizophrenia Patients with Metabolic Syndrome

2022· article· en· W4285740935 on OpenAlexaboutno aff
Xueying Zhang, Chen He, Peijun Ju, Qingrong Xia, Jianliang Gao, Loufeng Zhang, Xuequan Chen, Hui Yuan, Hua Gao, Yang Zhang, Junwei Yan, Wen Xie, Cuizhen Zhu

Bibliographic record

VenueNeuropsychiatric Disease and Treatment · 2022
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
FundersAnhui Provincial Key Research and Development PlanAnhui Medical University
KeywordsMetabolic syndromeMedicinePositive and Negative Syndrome ScaleCognitionWaistVerbal fluency testSchizophrenia (object-oriented programming)Clinical psychologyInternal medicinePsychiatryNeuropsychologyPsychosisObesity

Abstract

fetched live from OpenAlex

Purpose: The study aimed to clarify the cognitive impairments of schizophrenia with metabolic syndrome while evaluating their potential as risk factors. Patients and Methods: We recruited 153 participants and divided them into three groups according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, criteria and the guideline standards for the prevention and treatment of dyslipidemia in Chinese adults in 2007 for metabolic syndrome, as follows: healthy control group (n = 47); nonmetabolic syndrome group (n = 58); and metabolic syndrome group (n = 48). Psychotic symptoms were evaluated using the Positive and Negative Syndrome Scale. Cognitive function and automatic thinking were estimated using the Montreal Cognitive Assessment Scale, Verbal Fluency Test, and Automatic Thoughts Questionnaire. Serum biochemical parameters were measured by automatic biochemistry analyzer. Results: One-way ANOVA analysis revealed that differential cognition impairments in schizophrenia patients compared to controls. Furthermore, results of multiple comparisons showed that more serious barriers in orientation, language fluency, and negative automatic thinking existed in the metabolic syndrome group than in the healthy and non-metabolic syndrome groups. Spearman correlation and stepwise linear regression analyses showed that psychopathological symptoms, high waist circumference, and high triglyceride were the predictive factors for negative automatic thoughts, orientation, and language fluency. Those results collectively revealed that high waist circumference, high triglyceride and negative automatic thinking had validity and effectiveness in predicting the cognitive function impairments of the metabolic syndrome group. Conclusion: The present findings strongly supported the notion that aberrant parameters of high waist circumference, high triglyceride and high negative automatic thoughts had validity and effectiveness predictive role for cognitive impairments in the schizophrenics with metabolic syndrome. The schizophrenia patients with metabolic syndrome should receive regular monitoring and adequate treatment for metabolic and psychological risk factors.

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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.007
GPT teacher head0.220
Teacher spread0.213 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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