Post marketing surveillance of two St. John's wort and four liquorice products in Iran's market
Bibliographic record
Abstract
With the tremendous expansion in the use of herbal medicine worldwide, safety and efficacy as well as quality control of herbal medicine have become important concerns for both health authority and public. In this paper, the analysis of two St. John's wort (Hypericum perforatum) products and four liquorice (Glycyrrhiza glabra) products by spectrophotometric and HPLC methods, is explained. Moreover chromatographic fingerprinting was done by TLC scanner. Hypericin was chosen as the marker of St. John's wort products and 18 β-glycyrrhetinic acid was chosen as the marker for post marketing survey of four liquorice commercial formulations. The content of total hypericins that can be expressed as hypericin exist in the sample was 0.008% (mg/100mg) in product A/1 and 0.01% in product B/2. According to the analyses, the content of 18 β-glycyrrhetinic acid in the samples were between 0.002-0.05% (mg/100mg). It is suggested that manufacturers should commit to proper quality control procedures and ensure that label claims for content and dosage are accurate and realistic.
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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 | 0.030 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".