Herbal Medicine: An Overview of the Literature From Three Decades
Bibliographic record
Abstract
OBJECTIVES: To generate information about what types of articles have been published in what languages on what topics. In addition, we assessed the methodological quality of randomized clinical trials (RCTs) over time. METHODS: Searches were carried out in Medline in order to (a) identify the number of articles published in English, Chinese, German, Russian, and Japanese during the years 1977, 1987, 1997, and 2007; (b) identify the number of five types of articles, namely, systematic reviews (SRs), reviews, clinical controlled trials (CCTs), RCTs, and case reports, published in the above-mentioned 4 years; (c) identify the types of herbal medicine that were most frequently tested in CCTs and RCTs. Twenty RCTs were retrieved from each of the above-mentioned 4 years and were quality assessed using Jadad score and were statistically compared. RESULTS: (1) The number of articles on herbal medicine has increased during the last three decades. (2) Review articles and RCTs were the most numerous types of publications toward the end of our observation period; CCTs were the least. (3) Guar gum, evening primrose oil, ginseng, and Ginkgo biloba were some of the main herbal medicines that were most frequently submitted to CCTs and RCTs. (4) The average Jadad score improved continually (F = 6.27, p = .014) between 1977 and 2007. CONCLUSION: These findings are consistent with the notion that the quantity and quality of RCTs of herbal medicine have been improving during the last three decades. We suggest that the adaption of the herbal-medicine-specific Consolidated Standard of Reporting Trials guidelines might further improve the situation in future.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.050 | 0.039 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".