The effects of spirulina on glycemic control and serum lipoproteins in patients with metabolic syndrome and related disorders: A systematic review and meta‐analysis of randomized controlled trials
Why this work is in the frame
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Bibliographic record
Abstract
The aim of this systematic review and meta‐analysis was to evaluate the effects of spirulina on glycemic control and serum lipoproteins in patients with metabolic syndrome (MetS) and related disorders. Two independent authors systematically searched online database including EMBASE, Scopus, PubMed, Cochrane Library, and Web of Science from inception until April 30, 2019. The Cochrane Collaboration's risk of bias tool was applied to assess the methodological quality of included trials. The heterogeneity among the included studies was assessed using Cochrane's Q test and I ‐square ( I 2 ) statistic. Pooling effect sizes from studies showed a significant reduction in fasting plasma glucose (FPG; weighted mean difference [WMD]: −10.31; 95% confidence interval, CI [−16.21, −4.42]) and insulin concentrations (WMD: −0.53; 95% CI [−0.62, −0.44]) following the administration of spirulina. Pooled analysis showed also a significant reduction in total cholesterol (WMD: −20.50; 95% CI [−38.25, −2.74]), low‐density lipoprotein cholesterol (LDL‐C; WMD: −19.02; 95% CI [−36.27, −1.78]), and very low‐density lipoprotein cholesterol (VLDL‐C) concentrations (WMD: −6.72; 95% CI [−9.19, −4.26]) and a significant increase in high‐density lipoprotein cholesterol (HDL‐C) levels (WMD: 1.42; 95% CI [0.16, 2.68]) following spirulina therapy. This meta‐analysis demonstrated the beneficial effects of spirulina supplementation on improving FPG, insulin, total cholesterol, LDL‐C, VLDL‐C, and HDL‐C levels in patients with MetS and related disorders.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.056 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it