A Systematic Quantitative Analysis of the Literature of the High Variability in Ginseng (<i>Panax</i> spp.)
Bibliographic record
Abstract
Herbs have experienced an unprecedented surge in popularity (1). This has occurred in the absence of adequate safety and efficacy evidence, prompting calls for rigorous clinical assessments (2). Complicating these assessments is compositional variability. This is a concern with one of the most popular herbs, ginseng (3). The principal reference components, to which pharmacological effects have been attributed, are its ginsenosides (steroidal glycosides). We undertook a systematic quantitative analysis of the literature to assess the coefficient of variation (CV) in ginsenosides across species, assay technique, and ginsenoside type. The PubMed (1966-present), EMBASE (1980-present), HealthSTAR (1975-present), Cochrane library (issue 2, 2002), and AGRICOLA (1979-present) databases were searched using “ginsenosides AND (chromatography OR HPLC OR HPTLC OR TLC OR LC OR DCC OR GC OR ELISA OR UV OR MS OR NMR OR ELSD)”. One-hundred eleven articles were identified. Two reviewers applied three inclusion criteria: publication quality: peer-reviewed; end point: quantitative ginsenoside concentrations; and ginseng …
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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 | 0.047 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.043 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".