Deep brain stimulation from past to future: research trends and global outcomes with bibliometric analysis during 1980-2022
Bibliographic record
Abstract
Aims: We aimed to summarize the intellectual structure of the deep brain stimulation (DBS), to reveal the global productivity, to identify and map the latest trends by analysing the social and structural relationships between the different research components of scientific articles published on DBS. Methods: 5939 articles on DBS published during 1980 and 2022 were analysed utilized various statistical approaches. Network visualization maps were created to reveal trend topics, citation analysis, and international collaborations. Spearman's correlation analysis was used for correlation investigations. The exponential smoothing predictor was used to determine the article productivity trend. Results: The most prolific author on DBS was Okun, Michael S. (209 articles) and the most productive institution was the University of Toronto (n=283). The top 3 productive countries were United States of America (n=2371, 39.9%), Germany (910, 15.3%), and United Kingdom (550, 9.2%). From past to present, the most studied topics were Parkinson's disease, subthalamic nucleus DBS, dystonia, globus pallidus, essential tremor, movement disorders, thalamus, functional neurosurgery, neuromodulation, depression, obsessive compulsive disorder, basal ganglia. Conclusion: The primary trend topics that have been studied more in recent years are tractography, freezing of gait, Parkinson’s disease, Parkinson’, Parkinson#apos, autonomy, self, machine learning, non-motor symptoms, functional connectivity, globus pallidus interna, volume of tissue activated, adaptive deep brain stimulation, beta oscillations, medial forebrain bundle, and local field potential. The secondery trend topics were optogenetics, pediatric, frameless, closed-loop DBS, refractory epilepsy, satellite broadcasting, asleep DBS, optimization, biomarker, directional Leeds, nucleus bassals of meynert, personality, authenticity, and anterior nucleus of thalamus.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.033 | 0.057 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".