Research hotspots and trends in the interaction mechanisms of neuroinflammation and sleep disorders: A bibliometric analysis based on WOS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.056 | 0.161 |
| 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 it