Beyond the Surface: Tracing the Evolution of Inflammatory Mechanism in Depression through Bibliometric Analysis
Bibliographic record
Abstract
BACKGROUND: Depression is a common mental illness that has become a major economic burden worldwide. Recently, increasing evidence has highlighted the inflammatory mechanism of depression. In order to understand the research status of this field, this study used the bibliometric analysis method to overview the research content and progress, as well as analyze the development trend and limitations. METHODS: In this study, articles and reviews were included in the specific search strategy. The matched papers were exported from the Web of Science database. CiteSpace 6.3 R1 and Bibliometrix (R package) were utilized to generate bibliometric and knowledge maps. RESULTS: A total of 25,063 articles were included in this study. The number of publications in this field has gradually increased, especially in recent years. These papers come from 156 countries, led by the United States and China mainland. The leading research institution is the University of Toronto (Canada). Brain Behavior and Immunity is the journal with the most publications and the most frequently co-cited journals. Among 91,100 authors, Maes M has the most publications and co-citations. According to the keywords burst and co-cited reference analysis, the hotspots in the field in recent years include kynurenine, c-reactive protein, neuroinflammation, and gut microbiota. CONCLUSION: Although abundant academic achievements have been made on the inflammatory mechanism of depression, there is still a long way to go before these research results can be applied to clinical practice. Strengthening international academic exchanges and cooperation may promote breakthroughs in this field.
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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 | 0.010 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.107 | 0.141 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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, 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".