A bibliometric and visual analysis of cognitive function in bipolar disorder from 2012 to 2022
Bibliographic record
Abstract
Abstract Introduction Bipolar disorder (BD) is a chronic psychiatric disorder that combines hypomania or mania and depression. The study aims to investigate the research areas associated with cognitive function in bipolar disorder and identify current research hotspots and frontier areas in this field. Methodology Publications related to cognitive function in BD from 2012 to 2022 were searched on the Web of Science Core Collection (WoSCC) database. VOSviewer, CiteSpace, and Scimago Graphica were used to conduct this bibliometric analysis. Results A total of 989 articles on cognitive function in BD were included in this review. These articles were mainly from the United States, China, Canada, Spain and the United Kingdom. Our results showed that the journal “Journal of Affective Disorders” published the most articles. Apart from “Biploar disorder” and “cognitive function”, the terms “Schizophrenia”, “Meta analysis”, “Rating scale” were also the most frequently used keywords. The research on cognitive function in bipolar disorder primarily focused on the following aspects: subgroup, individual, validation and pathophysiology. Conclusions The current concerns and hotspots in the filed are: “neurocognitive impairment”, “subgroup”, “1st degree relative”, “mania”, “individual” and “validation”. Future research is likely to focus on the following four themes: “Studies of the bipolar disorder and cognitive subgroups”, “intra-individual variability”, “Validation of cognitive function tool” and “Combined with pathology or other fields”.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.248 | 0.250 |
| 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) | 0.011 | 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".