A Bibliometric Analysis of Medical Research Literature on Commonly Sold Herbal Medicines
Bibliographic record
Abstract
Abstract Background Herbal medicines and supplements are frequently utilized for healthcare purposes. Due to their increased use globally, it is essential to understand the characteristics of research conducted on this topic. Methods Search strategies were created by identifying the top-selling herbal supplements from the 2020 HerbalGram Market Report. The Natural Medicines database was used to identify and record the most common terms used to refer to the herbal supplements. The search strategy was limited to the “MEDICINE” category. Searches were run on Scopus on August 02, 2021, and all results were exported on the same day to avoid discrepancies due to daily database updates. Various bibliometric data were collected, including information on total number of publications, publications per year, number of authors and journals, open access status, document type, author affiliations, most highly published authors, institutional affiliations, funding sponsors, country of publication, and most highly cited publications. VOSViewer, a software tool, was used to construct and visualize the bibliometric networks. Results A total of 42 385 (12 481 open access) articles published by 92 814 unique authors between 1827 and 2021 were obtained. An overall upward trend has been noticed in the number of publications, with the most widely researched herbal medicines being wheatgrass, turmeric, barley, garlic, and green tea. The most productive countries were the United States (n=6957) and China (n=5426). Planta Medica published the largest number of publications related to herbal medicine. Conclusions A continuous upward trend has been identified in the number of publications surrounding commonly sold herbal medicines. Due to the projected increase of the use of these medicines, future research should examine and analyse the characteristics of emerging publications in this field.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.021 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.262 | 0.306 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.000 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".