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Record W2924113793 · doi:10.1080/10408398.2019.1584873

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

2019· review· en· W2924113793 on OpenAlexaff
Sean Wharton, Revi Bonder, Aaron Jeffery, Rebecca Christensen

Bibliographic record

VenueCritical Reviews in Food Science and Nutrition · 2019
Typereview
Languageen
FieldMedicine
TopicPharmacology and Obesity Treatment
Canadian institutionsMaple Leaf Medical ClinicYork University
FundersNational Institutes of Health
KeywordsWeight lossObesityMedicineGlucomannanNutraceuticalTraditional medicineInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.290
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.053
GPT teacher head0.416
Teacher spread0.364 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations39
Published2019
Admission routes1
Has abstractyes

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