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Record W2791254434 · doi:10.1080/10408398.2018.1427044

The effects of ginger intake on weight loss and metabolic profiles among overweight and obese subjects: A systematic review and meta-analysis of randomized controlled trials

2018· review· en· W2791254434 on OpenAlexaff
Najmeh Maharlouei, Reza Tabrizi, Kamran Bagheri Lankarani, Abbas Rezaianzadeh, Maryam Akbari, Fariba Kolahdooz, Maryam Rahimi, Fariba Keneshlou, Zatollah Asemi

Bibliographic record

VenueCritical Reviews in Food Science and Nutrition · 2018
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicGinger and Zingiberaceae research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineMeta-analysisOverweightInternal medicineStrictly standardized mean differenceConfidence intervalBody mass indexRandomized controlled trialWeight lossGlycemicPooled varianceWaistObesityInsulin

Abstract

fetched live from OpenAlex

This systematic review and meta-analysis of randomized controlled trials (RCTs) was performed to summarize the effect of ginger intake on weight loss, glycemic control and lipid profiles among overweight and obese subjects. We searched the following databases through November 2017: MEDLINE, EMBASE, Web of Science, and Cochrane Central Register of Controlled Trials. The relevant data were extracted and assessed for quality of the studies according to the Cochrane risk of bias tool. Data were pooled using the inverse variance method and expressed as Standardized Mean Difference (SMD) with 95% Confidence Intervals (95% CI). Heterogeneity between studies was assessed by the Cochran Q statistic and I-squared tests (I2). Overall, 14 studies were included in the meta-analyses. Fourteen RCTs with 473 subjects were included in our meta-analysis. The results indicated that the supplementation with ginger significantly decreased body weight (BW) (SMD −0.66; 95% CI, −1.31, −0.01; P = 0.04), waist-to-hip ratio (WHR) (SMD −0.49; 95% CI, −0.82, −0.17; P = 0.003), hip ratio (HR) (SMD −0.42; 95% CI, −0.77, −0.08; P = 0.01), fasting glucose (SMD −0.68; 95% CI, −1.23, −0.05; P = 0.03) and insulin resistance index (HOMA-IR) (SMD −1.67; 95% CI, −2.86, −0.48; P = 0.006), and significantly increased HDL-cholesterol levels (SMD 0.40; 95% CI, 0.10, 0.70; P = 0.009). We found no detrimental effect of ginger on body mass index (BMI) (SMD −0.65; 95% CI, −1.36, 0.06; P = 0.074), insulin (SMD −0.54; 95% CI, −1.43, 0.35; P = 0.23), triglycerides (SMD −0.27; 95% CI, −0.71, 0.18; P = 0.24), total- (SMD −0.20; 95% CI, −0.58, 0.18; P = 0.30) and LDL-cholesterol (SMD −0.13; 95% CI, −0.51, 0.24; P = 0.48). Overall, the current meta-analysis demonstrated that ginger intake reduced BW, WHR, HR, fasting glucose and HOMA-IR, and increased HDL-cholesterol, but did not affect insulin, BMI, triglycerides, total- and LDL-cholesterol levels.

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 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)0.021
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.026
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.045
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0260.042
Bibliometrics0.0110.009
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0030.002
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 head0.221
GPT teacher head0.517
Teacher spread0.296 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations93
Published2018
Admission routes1
Has abstractyes

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