Cohort Studies on Chronic Non-communicable Diseases Treated With Traditional Chinese Medicine: A Bibliometric Analysis
Bibliographic record
Abstract
Cohort studies investigating the treatment of chronic non-communicable diseases (NCDs) with traditional Chinese medicine (TCM) have considerably accumulated in recent years. To systematically and for the first time present the achievements and dilemmas of cohort studies, strict inclusion and exclusion criteria were used to search publications from the Web of Science, PubMed, Embase, Cochrane Library, and China National Knowledge Infrastructure databases for cohort studies on NCDs with TCM since the establishment of these databases. Information on the year of publication, exposure factors, diseases, and outcome indicators was obtained, and a literature quality assessment and bibliometric descriptive analysis were conducted. A total of 182 published articles involving 1,615,106 cases were included. There were 110 non-prospective cohort studies and 72 prospective cohort studies. The diseases involved in the cohort studies were, in the order of the number of published articles, malignant tumors (82 articles, 45.05%), cardiovascular diseases (35 articles, 19.23%), neurological diseases (29 articles, 15.93%), chronic kidney diseases (16 articles, 8.79%), liver cirrhosis (8 articles, 4.40%), diabetes mellitus (8 articles, 4.40%), and chronic respiratory diseases (4 articles, 2.20%). The study participants were mainly from China (177 articles, 97.25%). The number of cohort studies increased significantly in the last 5 years (65 articles, 35.71%), and following the Newcastle-Ottawa Scale (NOS) literature quality evaluation, the number of articles that received a score of four to five was high (116 articles, 63.73%), and the overall quality needs to be improved. The application of cohort studies in the field of TCM for the prevention and treatment of NCDs has developed rapidly in the past 5 years, focusing on the prevention and treatment of tumors as well as cardiovascular and cerebrovascular diseases. However, the design and implementation of cohort studies still have considerable limitations. To provide more clinical evidence, researcher should actively cooperate with evidence-based methodologists and standardize the implementation of cohort studies.
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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 | 0.025 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.175 | 0.193 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".