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Record W4411347434 · doi:10.1038/s43856-025-00944-1

Relationship between grammar and schizophrenia: a systematic review and meta-analysis

2025· review· en· W4411347434 on OpenAlexafffundabout
Dalia Elleuch, Yinhan Chen, Qiang Luo, Lena Palaniyappan

Bibliographic record

VenueCommunications Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsRobarts Clinical TrialsDouglas Mental Health University InstituteWestern University
FundersFonds de Recherche du Québec - SantéProgram of Shanghai Academic Research LeaderNational Key Research and Development Program of ChinaCanadian Institutes of Health ResearchCanada First Research Excellence FundNational Natural Science Foundation of ChinaGovernment of CanadaWellcome TrustMcGill UniversityWellcome
KeywordsPsycINFOMeta-analysisSyntaxSchizophrenia (object-oriented programming)PsychologyComprehensionClinical psychologyCognitive psychologyDevelopmental psychologyMEDLINEMedicineNatural language processingLinguisticsPsychiatryComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Schizophrenia significantly impairs everyday communication, affecting education and employment. Such communication difficulties may arise from deficits in syntax-understanding and generating grammatical structures. Research on syntactic impairments in schizophrenia is underpowered, with inconsistent findings, and it is unclear if deficits are specific to certain patient subgroups, regardless of symptom profiles, age, sex, or illness severity. METHODS: A pre-registered (Open Science Framework: https://doi.org/10.17605/OSF.IO/7FZUC ) search using PubMed, Scopus, PsycINFO, and Web of Science databases up to May 1, 2024, for all studies investigating syntax comprehension and production in schizophrenia vs. healthy controls. Excluding studies on those <18 years of age and qualitative research, we extracted Cohen's d and log coefficient of variation ratio and used Bayesian meta-analysis across 6 domains: 2 in comprehension and 4 in production in patient-control comparisons. Study quality was evaluated using a modified Newcastle-Ottawa Scale, with moderators (age, sex, study quality, language) tested via meta-regression. RESULTS: We identify 86 relevant articles, of which 45 have sufficient data for meta-analysis (n = 2960 participants, 64.4% English, weighted mean age(sd) = 32.3(5.6)). Bayesian meta-analysis shows strong evidence of syntactic deficits in schizophrenia across all domains (d = 0.65-1.01, overall random-effects d = 0.86, 95% CrI [0.67-1.03]), with syntax comprehension being most affected, with weak publication bias. People with schizophrenia show increased variability in comprehension and production of long and complex utterances (lnCVR = 0.21, 95% CrI [0.07-0.36]), hinting at subgroups with differing performance. CONCLUSIONS: Robust impairments in grammatical comprehension and production in schizophrenia suggest opportunities for targeted interventions focusing on syntax, a rule-based feature amenable to cognitive, educational, and linguistic 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)0.014
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.035
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0160.033
Bibliometrics0.0090.010
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.290
GPT teacher head0.464
Teacher spread0.174 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations13
Published2025
Admission routes3
Has abstractyes

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