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Record W4224109889 · doi:10.1155/2022/2967977

Efficacy of the Synbiotic Supplementation on the Metabolic Factors in Patients with Metabolic Syndrome: A Randomized, Triple‐Blind, Placebo‐Controlled Trial

2022· article· en· W4224109889 on OpenAlexaff
Fatemeh Rahimi, Yahya Pasdar, Mojtaba Kaviani, Soheil Abbasi, Hillary Fry, Azita Hekmatdoost, Omid Nikpayam, Golbon Sohrab, Mansoaur Rezaei, Seyed Mostafa Nachvak, Reza Mohammadi

Bibliographic record

VenueInternational Journal of Clinical Practice · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsAcadia University
FundersKermanshah University of Medical Sciences
KeywordsMedicineMetabolic syndromePlaceboGlycemicInternal medicineRandomized controlled trialAnthropometryPlacebo-controlled studySynbioticsLipid profileGastroenterologyPhysical therapyProbioticObesityInsulinCholesterolDouble blind

Abstract

fetched live from OpenAlex

Background: Metabolic syndrome is overwhelmingly increasing and is a significant risk factor for cardiovascular disorder, so effective treatment strategies are considered high priority. This study aimed to determine the effects of synbiotic supplementation on metabolic factors in patients with metabolic syndrome. Methods: In this triple-blind, randomized, placebo-controlled, clinical trial, 108 participants were divided into two groups to receive synbiotic supplementation or placebo for 12 weeks. All participants were also educated about maintaining a healthy lifestyle and consuming low-calorie nutritious meals, along with dietary intake and physical activity monitoring. Anthropometric measures, blood pressure, glycemic indices, lipid profile, hepatic enzymes, and hs-CRP were evaluated at the baseline and end of the trial. Results: =0.007], but there was no difference between groups in other metabolic factors. Conclusions: These findings suggest that synbiotic supplementation while following a healthy lifestyle and nutrition improved FBG in patients with metabolic syndrome.

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)0.005
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.380
Teacher spread0.353 · 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.

Study designRandomized trial
Domainnot available
GenreEmpirical

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

Citations17
Published2022
Admission routes1
Has abstractyes

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