The safety and effectiveness of commonly-marketed natural supplements for weight loss in populations with obesity: A critical review of the literature from 2006 to 2016
Bibliographic record
Abstract
Objective: To evaluate the evidence published from 2006 to 2016 on the effectiveness and safety of commonly used natural supplements for weight loss in individuals with obesity.Methods: Amazon and Google were searched for names of mono-agent natural supplements marketed for weight loss and a list of the 10 supplements was created. Google Scholar, Pubmed, Science Direct, and the Cochrane Library were searched for articles that met inclusion.Results: At least one article was published on the effectiveness or safety of bitter orange, capsinoid, carnitine, chromium picolinate, Coleus forskohlii, conjugated linoleic acid, glucomannan, green tea and psyllium for weight loss in populations with obesity from 2006 to 2016. There was insufficient evidence to suggest that the natural supplements examined contribute to significant weight loss, with the exception of perhaps glucomannan in the form of PGX. In general, the majority of side-effects reported were minor to moderate, and gastrointestinal-related. However, in some cases extreme side-effects such as liver and kidney failure were observed.Conclusion: Contrary to popular belief, results of this review suggest that the use of natural supplements for weight loss are unlikely to contribute to meaningful weight loss and in some cases may contribute to harm.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".