Bibliometric analysis of the intestinal microbiota and demyelinating diseases, particularly multiple sclerosis, since 2014
Bibliographic record
Abstract
Background: The gut-brain axis (GBA) represents a complex, bidirectional communication network that connects the central nervous system (CNS) and the gastrointestinal system. Our study aimed to explore the correlation between the intestinal microbiota and demyelinating diseases from a bibliometric perspective, focusing on research since 2014. Methods: A comprehensive search was carried out on the Web of Science Core Collection (WoSCC) to locate studies on the intestinal microbiota and demyelinating diseases, with a focus on publications from 1 January 2014 to 29 March 2024. We visualized and analyzed the data using VOSviewer, CiteSpace, and Charticulator. Results: We gathered 429 scholarly articles on the intestinal microbiota and demyelinating disorders published in the past 10 years. Research concerning the intestinal microbiota and demyelinating diseases has demonstrated a consistent increase in frequency over time. The USA has the highest number of publications, while Canada has the highest average number of citations, reaching as high as 3,429, which is greater than that of the USA. Moreover, the journal with the highest number of publications was Frontiers in Immunology, with 33 publications and 1,494 citations. The majority of the scholars focused on "multiple sclerosis" and "gut microbiota," which are the primary keywords in the field of the intestinal microbiota and demyelinating diseases. Conclusion: This study conducted a comprehensive analysis of existing research investigating the correlation between the intestinal microbiota and demyelinating diseases. Using advanced bibliometric tools such as VOSviewer and CiteSpace, this study analyzed the intricate relationship between the intestinal microbiota and the pathogenesis of demyelinating conditions. In addition, the study used literature statistical analysis to identify research hotspots and future directions in the field.
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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 | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.009 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.153 | 0.222 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".