Emerging trends in gene and bipolar disorder research: a bibliometric analysis and network visualisation
Bibliographic record
Abstract
This study aims to use a bibliometric technique to evaluate the scientific output of gene and bipolar disorder research. The search query related to gene and bipolar disorder from the Scopus database identified 1848 documents from 1951 to 2020. The growth in the publications increased since early 1990, peaked in 2011, and started to decline thereafter. High occurrence in author keywords suggests that some research topics, such as "polymorphism", "linkage" and "association study" have waned over time, whereas others, such as "DNA methylation," "circadian rhythm," "" and "meta-analysis," are now the emerging trends in gene and bipolar disorder research. The USA was the country with the highest production followed by the UK, Canada, Italy and Germany. The leading institutions were Cardiff University in the UK, the National Institute of Mental Health (NIMH) in the USA, King's College London in the UK and the University of California, San Diego in the USA. The leading journals publishing gene and bipolar literature were the American Journal of Medical Genetics Neuropsychiatric Genetics, Molecular Psychiatry and Psychiatric Genetics. The top authors in the number of publications were Craddock N, Serretti A and Rietschel M. According to the co-authorship network analysis of authors, the majority of the authors in the same clusters were closely linked together and originated from the same or neighbouring country. The findings of this study may be useful in identifying emerging topics for future research and promoting research collaboration in the field of genetic studies related to bipolar disorder.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.109 | 0.111 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".