International Technologies on Prevention and Treatment of Neurological and Psychiatric Diseases: Bibliometric Analysis of Patents
Bibliographic record
Abstract
BACKGROUND: Neurological and psychiatric disorders are serious and expensive global public health problems. Therefore, exploring effective intervention technologies plays an important role in improving patients' clinical symptoms and social functions, as well as reducing medical burden. OBJECTIVE: The aim of this study is to analyze and summarize the key new technologies and innovative development trends witnessed globally for neurological illness and psychiatric disorders by mining the relevant patent data. METHODS: A bibliometric analysis was conducted on patent applications, priority countries, main patentees, hot technologies, and other patent information on neurological and psychiatric disorders, revealing the current situation along with the trend of technology development in this field. RESULTS: In recent years, inventions and innovations related to neurological and psychiatric diseases have become very active, with China being the largest patent priority country. Of the top patent holders, Visicu (headquartered in the United States) is the leader. The distribution of patent holders in China remains relatively scattered, with no monopoly organization at present. Global technologies on neurological illness and psychiatric disorders are mainly concentrated around A61B (diagnosis, surgery, and identification). CONCLUSIONS: This paper analyzed and summarized the key new technologies and global innovative development trends of neurological and psychiatric diseases by mining the relevant patent data, and provides practical references and research perspectives for the prevention and treatment of the aforesaid diseases.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.094 | 0.104 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".