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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 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)0.067
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0670.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.472
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