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Record W4401552166 · doi:10.7759/cureus.66733

Cumulative Effect of Psychosis and Aging on Cognitive Function in Patients Diagnosed With Schizophrenia Spectrum Disorders: A Cognitive Domain Approach

2024· article· en· W4401552166 on OpenAlexaboutno aff
Panagiotis Malliaris, Nikiforos V. Angelopoulos, Efthimios Dardiotis, Konstantinos Bonotis

Bibliographic record

VenueCureus · 2024
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMontreal Cognitive AssessmentCognitionSchizophrenia (object-oriented programming)Analysis of varianceCognitive declineSample size determinationPsychosisInternal medicinePediatricsDementiaCognitive impairmentPsychiatryDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Schizophrenia spectrum disorders are characterized by cognitive decline, which is evident even in the prodromal phase. Aging is a complex gradual procedure that affects, among other organs, the central nervous system, resulting in age-related cognitive decline. OBJECTIVE: The objective of this study is to assess the cognitive function of patients diagnosed with psychotic disorders, in comparison with healthy controls, along the age spectrum. METHODS: Sixty patients diagnosed with schizophrenia spectrum disorders in remission, 20-59 years old, and 60 healthy controls, matched by age and educational level, from the region of Thessaly in Central Greece, were evaluated, with respect to their cognitive performance, using the Greek version of the Montreal Cognitive Assessment (MoCA). Correlations between age and MoCA total and cognitive domains' scores, as well as statistical analysis of variance (ANOVA) and t-test among age groups, were performed using Statistical Product and Service Solutions (SPSS, version 23; IBM SPSS Statistics for Windows, Armonk, NY). RESULTS: The MoCA score was negatively correlated with age, both in the patients' group (p<0.001) and in the control group (p=0.001). A significant statistical difference in mean MoCA scores between patients and healthy controls was observed, not only in the total sample (p<0.001) but also in all age groups (20-29: p=0.006, 40-49: p=0.024, 50-59: p<0.001), except for age group 30-39 (30-39: p=0.356). Statistically significant differences were also found between patients and healthy controls in the total sample, regarding specific cognitive domains, in the visuospatial and executive function domain (p=0.01), attention domain (p<0.001), language domain (p<0.001), and orientation domain (p<0.005). Interestingly, different deterioration patterns in cognitive domains were observed in each age group. Specifically, in the age group 20-29, statistically significant differences were found between patients and healthy controls in the language domain (p<0.014) and orientation domain (p<0.041). No difference was found in the age group 30-39, while statistically significant differences were found between patients and healthy controls in the age group 40-49 in the attention domain (p<0.001) and language domain (p<0.001). Finally, in the age group 50-59, such differences were found in the visuospatial and executive function domain (p=0.041), attention domain (p=0.006), and language domain (p=0.001). Statistically significant cognitive decline occurs in a shorter period in the patients' group, suggesting an accelerated cognitive decline in psychotic patients after middle age. CONCLUSIONS: Age-related cognitive decline in psychotic patients occurs at an accelerated rate in relation to the control sample, with age-specific cognitive domain decline patterns, due to the cumulative effect of aging and psychosis on cognition. Further, larger, multicenter research should focus on establishing these results and designing relevant procognitive interventions.

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)0.001
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.010
GPT teacher head0.283
Teacher spread0.273 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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