Data from China: Analysis of Research Hotspots in the Field of Digital Therapeutics in the Chinese Context (1989–2021)
Bibliographic record
Abstract
Objective: China is globally leading in digitalization. However, due to language barriers, there are few international studies on digital medicine based on the Chinese context. This paper focuses on Chinese data to explore the distribution and characteristics of research hotspots in the field of digital therapy in China. Methods: By quantifying the published literature on digital therapy from 1989 to 2021 in the authoritative Chinese database CNKI, keyword maps were drawn to sort out specific research directions and hotspot distributions in the field of digital therapy in China, and to predict future research trends and challenges. The literature was retrieved from the CNKI database, exported, and converted into CiteSpace 5.7.R2 software for solving six quantitative indicators, clustering, and map drawing. Under the guidance and constraints of the results, the development context, research directions, core keywords, and hotspots in the field of digital therapy were analyzed. Results: A total of 458 articles were included. The clustering was credible (Q = 0.8379>0.3; mean S = 0.9447>0.6), and 19 significant research directions were obtained (S>0.6). Four high-frequency core keywords (F>33) and eight high-centrality core keywords (C>28) were identified; there were no prominent nodes. Conclusion: In the field of digital therapy represented by Chinese literature, there are four mainstream research directions: exploration of digital medical theoretical concepts, multi-field integration of digital medical extensions, digital applications in specific disease scenarios, and infrastructure and policy support. However, from the perspective of literature centrality and mutation indicators, a solid theoretical core and landmark technological breakthroughs have not yet been formed, highlighting the huge potential and challenges of future research and application in this field in the Chinese context.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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; a candidate call from one teacher head, 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".