Adverse events of herbal food supplements for body weight reduction: systematic review*
Bibliographic record
Abstract
Herbal weight-loss supplements are marketed with claims of effectiveness. Our earlier systematic review identified data from double-blind, randomized controlled trials for a number of herbal supplements. The aim of this systematic review was to assess all clinical evidence of adverse events of herbal food supplements for body weight reduction for which effectiveness data from rigorous clinical trials exist. We assessed Ephedra sinica, Garcinia cambogia, Paullinia cupana, guar gum, Plantago psyllium, Ilex paraguariensis and Pausinystalia yohimbe. Literature searches were conducted on Medline, Embase, Amed and The Cochrane Library. Data were also requested from the spontaneous reporting scheme of the World Health Organization. We hand-searched relevant medical journals and our own files. There were no restrictions regarding the language of publication. The results show that adverse events including hepatic injury and death have been reported with the use of some herbal food supplements. For herbal ephedra and ephedrine-containing food supplements an increased risk of psychiatric, autonomic or gastrointestinal adverse events and heart palpitations has been reported. In conclusion, adverse events are reported for a number of herbal food supplements, which are used for reducing body weight. Although the quality of the data does not justify definitive attribution of causality in most cases, the reported risks are sufficient to shift the risk-benefit balance against the use of most of the reviewed herbal weight-loss supplements. Exceptions are Garcinia cambogia and yerba mate, which merit further investigation.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".