Bibliometric Analysis on GABA-A Receptors Research Based on CiteSpace and VOSviewer
Bibliographic record
Abstract
Background: GABA-A receptors are the primary mediators of brain inhibitory neurotransmission. In the past years, many studies focused on this channel to decipher the pathogenesis of related diseases but lacked bibliometric analysis research. This study aims to explore the research status and identify the research trends of GABA-A receptor channels. Methods: Publications related to GABA-A receptor channels were retrieved from the Web of Science Core Collection from 2012 to 2022. After screening, the VOSviewer 1.6.18 and Citespace 5.8 R3 were used for bibliometric analysis from journals, countries, institutions, authors, co-cited references and keywords. Results: We included 12,124 publications in the field of GABA-A receptor channels for analysis. The data shows that although there was a slight decrease in annual publications from 2012 to 2021, it remained at a relatively high level. Most publications were in the domain of neuroscience. Additionally, the United States was the most prolific country, followed by China. Univ Toronto was the most productive institution, and James M Cook led essential findings in this field. Furthermore, brain activation, GABAAR subunits expression, modulation mechanism in pain and anxiety behaviors and GABA and dopamine were paid attention to by researchers. And top research frontiers were molecular docking, autoimmune encephalitic series, obesity, sex difference, diagnosis and management, EEG and KCC2. Conclusion: Taken together, academic attention on GABA-A receptor channels was never neglected since 2012. Our analysis identified key information, such as core countries, institutions and authors in this field. Molecular docking, autoimmune encephalitic series, obesity, sex difference, diagnosis and management, EEG and KCC2 will be the future research direction.
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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 | 0.033 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.055 | 0.098 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".