The schizophrenia and gut microbiota: A bibliometric and visual analysis
Bibliographic record
Abstract
Background: Many studies have explored the link between the gut microbiota and schizophrenia. To date, there have been no bibliometric analyses to summarize the association between the gut microbiota and schizophrenia. We aimed to conduct a bibliometric study of this association to determine the current status and areas for advancement in this field. Materials and methods: Publications related to the gut microbiota and schizophrenia were retrieved from the Web of Science Core Collection (WoSCC). The WoSCC literature analysis wire and VOSviewer 1.6.16 were used to conduct the analysis. Results: In total, 162 publications were included in our study. The publications generally showed an upward trend from 2014. A total of 873 authors from 355 organizations and 40 countries/regions contributed to this field. The leading authors were Timothy Dinan, John F Cryan, and Emily Severance. The leading institutions were Johns Hopkins University, the University College Cork, and the University of Toronto. The most productive countries were the United States (US), China, and Canada. In total, 95 journals contributed to this field. Among them, the top three productive journals were Schizophrenia Research, Progress in Neuro Psychopharmacology Biological Psychiatry, and Frontiers in Psychiatry. The important keywords in the clusters were gut microbiome, bipolar disorder, schizophrenia, antipsychotics, weight gain, metabolic syndrome, gut-brain axis, autism, depression, inflammation, and brain. Conclusion: The main research hotspots involving the connection between schizophrenia and the gut microbiota were the characteristics of the microbiota composition in schizophrenia patients, the gut-brain axis, and microbial-based interventions for schizophrenia. The studies about the association between gut microbiota and schizophrenia are limited, and more studies are needed to provide new insights into the gut microbiota in the pathogenesis and treatment of schizophrenia.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".