Effects of <scp><i>Nigella sativa</i></scp> on glycemic control, lipid profiles, and biomarkers of inflammatory and oxidative stress: A systematic review and meta‐analysis of randomized controlled clinical trials
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.
Bibliographic record
Abstract
The aim of this systematic review and meta‐analysis was to evaluate the effects of Nigella sativa ( N. sativa ) on glycemic control, lipid profiles, and biomarkers of inflammatory and oxidative stress. Two independent authors systematically examined online databases consisting of, EMBASE, Scopus, PubMed, Cochrane Library, and Web of Science from inception until October 30, 2019. Cochrane Collaboration risk of bias tool was applied to assess the methodological quality of the studied trials. The heterogeneity among the included studies were assessed using the Cochrane's Q test and I ‐square ( I 2 ) statistic. Data were pooled using a random‐effects model and weighted mean difference (WMD) was considered as the overall effect size. A total of 50 trials were included in this meta‐analysis. We found a significant reduction in total cholesterol (WMD: −16.80; 95% CI: −21.04, −12.55), triglycerides (WMD: −15.73; 95% CI: −20.77, −10.69), LDL‐cholesterol (WMD: −18.45; 95% CI: −22.44, −14.94) and VLDL‐cholesterol (WMD: −3.72; 95% CI: −7.27, −0.18) following supplementation with N. sativa . In addition, there was significant reductive effect observed with N. sativa on fasting glucose (WMD: −15.18; 95% CI: −19.82, −10.55) and HbA1C levels (WMD: −0.45; 95% CI: −0.66, −0.23). Effects of N. sativa on CRP (WMD: −3.61; 95% CI: −9.23, 2.01), TNF‐ α (WMD: −1.18; 95% CI: −3.23, 0.86), TAC (WMD: 0.31; 95% CI: 0.00, 0.63), and MDA levels (WMD: −0.95; 95% CI: −2.18, 0.27) were insignificant. This meta‐analysis demonstrated the beneficial effects of N. sativa on fasting glucose, HbA1c, triglycerides, total‐, VLDL‐, LDL‐cholesterol levels.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.048 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| 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