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Record W4417358357 · doi:10.1080/08989621.2025.2600404

Commercial funding of randomized controlled trials of weight-loss interventions using dietary supplements: A rapid review

2025· article· en· W4417358357 on OpenAlexaff
Jill R. Kavanaugh, Abigail Bulens, Julia A. Vitagliano, Meghan Harshaw, Amanda Raffoul, Nat Egan, S. Bryn Austin

Bibliographic record

VenueAccountability in Research · 2025
Typearticle
Languageen
FieldMedicine
TopicPharmacology and Obesity Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRandomized controlled trialPsychological interventionWeight lossMEDLINEClinical trialDietary supplement

Abstract

fetched live from OpenAlex

BACKGROUND: Nutrition research funded by commercial entities may be subject to bias. To date, no study has examined the prevalence of commercial funding in clinical trials of dietary supplements for weight loss. OBJECTIVE: To estimate the prevalence of commercial funding of randomized controlled trials (RCTs) of dietary supplement interventions for weight loss. METHODS: We conducted a rapid review of English-language RCTs published between 1 January 2023, testing dietary supplements for weight loss. Funding sources were extracted from full texts and categorized as industry, nonprofit, trade association, academic, government, or other. Commercial funders, trade associations, and nonprofits were further reviewed for ties to supplement sales. RESULTS: = 44) reported commercial funding, involving 64 unique funders and 118 instances of commercial involvement. More than half of funders sold dietary supplements or had affiliated companies that did, though some affiliations could not be verified due to limited transparency. No nonprofit funders had ties to supplement sales. CONCLUSIONS: The majority of RCTs evaluating dietary supplements for weight loss reported commercial funding. Further research is needed to assess whether such funding influences study findings.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Incentives · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewlow
gptMetaresearchMeta-epidemiology (narrow)
Domain: Incentives · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models splitAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.226
metaresearch head score (Gemma)0.588
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.954

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2260.588
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0080.009
Bibliometrics0.0190.018
Science and technology studies0.0010.003
Scholarly communication0.0090.010
Open science0.0030.004
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.472
GPT teacher head0.604
Teacher spread0.131 · 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

Labeled directly by 2 models reading the full record.

MetaresearchMeta-epidemiology (narrow)

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designSystematic review
DomainIncentives
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

Citations0
Published2025
Admission routes1
Has abstractyes

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