Research hotspots and trends in the interaction mechanisms of neuroinflammation and sleep disorders: A bibliometric analysis based on WOS
Bibliographic record
Abstract
This study aims to analyze the research hotspots and trends regarding neuroinflammation in sleep disorders over the past 30 years through bibliometric and review analyses. Relevant publications were sourced from the Web of Science Core Collection (WoSCC). We utilized VOSviewer and CiteSpace for the visualization and quantitative analysis of the literature to provide an objective presentation and predictions. A total of 2545 publications related to neuroinflammation and sleep disorders were identified, with the overall number of publications showing a continuous upward trend. Most of the publications originated from the United States and China. The University of Toronto, Harvard Medical School, and the University of California, Los Angeles, are leading institutions in this field. David Gozal and Michael R. Irwin are recognized as prominent figures in this area. The International Journal on Molecular Sciences and Brain Behavior and Immunity are the journals with the highest publication volume. Keywords and clustering analyses indicate that the current research in this field has developed a multidisciplinary integration pattern, with core trends focusing on the multi-axis regulation of neuroimmune interaction mechanisms, as well as individualized targeted intervention strategies based on biomarkers and gene editing. Additionally, the development of emerging technologies such as organoids and the establishment of multidisciplinary collaborative networks bring new hope for exploring the interactions between neuroinflammation and sleep disorders.
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How this classification was reachedexpand
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 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.190 | 0.201 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".