A Quantitative Assessment of the Reporting Quality of Herbal Medicine Research: The Road to Improvement
Bibliographic record
Abstract
OBJECTIVES: To quantify different aspects of the quality of reporting of herbal medicine clinical trials, to determine how that quality is affecting the conclusions of meta-analyses, and to target areas for improvement in future herbal medicine research reporting. STUDY DESIGN: The Electronic databases PubMed, Academic Search Premier, ScienceDirect, and Alt HealthWatch were searched for meta-analyses of herbal medicines in refereed journals and Cochrane Reviews in the years 2000-2004 and 2010-2014. The search was limited to meta-analyses of randomized controlled trials involving humans and published in English. Judgments and descriptions within the meta-analyses were used to report on risks of bias in the included clinical trials and the meta-analyses themselves. RESULTS: Out of 3264 citations, 9 journal-published meta-analyses were selected from 2000 to 2004, 116 from 2010 to 2014, and 44 Cochrane Reviews from 2010 to 2014. Across both time frames and categories of publication, <42% of the trials included in the meta-analyses described adequate randomization; <19% described concealment methods; <26% described double blinding; <29% described outcome assessment blinding, ≤53% discussed incomplete data, and <36% were nonselective in their reporting. Less than 54% of trials reported on adverse events and 64% of meta-analyses did not include a single trial with a low risk of bias. Taxonomic verification and chemical characterization of test products were infrequent in trials. Only 40% of meta-analyses considered publication bias and, of those that did, 90% found evidence for it. Cochrane Reviews were more likely than other sources to make negative conclusions of efficacy or to defer conclusions because of the absence of high quality trials. CONCLUSIONS: Meta-analyses of herbal medicines include a significant number of clinical trials that do not meet the recommended standards for clinical trial reporting. This quantitative assessment identified significant publication bias and other bias risks that may be due to inadequate trial design or incomplete reporting of outcomes. Suggested improvements to herbal medicine clinical trial reporting are discussed.
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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 | 0.873 | 0.930 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.015 |
| Bibliometrics | 0.035 | 0.035 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".