A bibliometric analysis of publications on obsessive–compulsive disorder using VOSviewer
Bibliographic record
Abstract
Background: Obsessive-compulsive disorder (OCD) is one of the top ten disabling diseases seriously affecting the health of population. Recently, studies on this disease significantly increased. However, only a few bibliometric analyses concerning this area have been reported. In this study, we used bibliometrics and visualization tools to examine the current state, hot topics and future trends in OCD research. Methods: Scientific publications regarding OCD were retrieved from the Web of Science Core Collection (WoSCC) database. The features of OCD research were further analyzed using VOSviewer. Results: A total of 24,552 publications and 65,296 authors in the field of OCD were retrieved from 2000 to 2022, showing an overall upward trend in publications over the past 22 years. One hundred and thirteen countries around the world had participated in the research. Among these countries, the developed countries such as the United States, England, and Canada were the crucial productive nations in this subject. As for institutions, the Harvard University, the University of London, and the University of California system were the leading institutions. Authors including Storch EA, Mataix-Cols D, and Stein DJ were the prolific authors. 1,949 journals are contributing to the OCD field, of which the top three are Biological Psychiatry (831 articles), European Neuropsychopharmacology (776 articles) and Psychiatric Research (648 articles). Research hotspots of OCD included pathogenesis, epidemiology, comorbidities, clinical features, and evaluation methods. COVID-19, mental health, functional connectivity, and genome-wide association were emerging trends in the field of OCD. Conclusion: This study integrates the bibliometric information on the current research status and emerging trends in OCD from a macro perspective. The findings can provide valuable insights into further research on OCD.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Bibliometrics | 0.196 | 0.414 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".