Maternal Serum Zinc Level and Pre-eclampsia Risk in African Women: a Systematic Review and Meta-analysis
Bibliographic record
Abstract
Some studies have reported the association between maternal serum zinc (Zn) levels and pre-eclampsia. However, many studies have reported controversial results. Hence, this systematic review and meta-analysis was planned to generate summarized evidence on the association between maternal serum Zn levels and pre-eclampsia in African women. Four electronic databases such as PubMed, Hinari, Google Scholar, and African Journals Online were searched for studies published in English. Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument, and Newcastle-Ottawa Scale were used for data extraction and quality assessment of the included studies. The meta-regression analysis was performed by the Stata 14 software. The standardized mean difference (SMD) values of lipid profiles were computed to assess their association with pre-eclampsia at 95% CI. A total of 12 observational studies were included. The mean values of serum Zn level were significantly lower in pre-eclamptic women as compared with normotensive pregnant women (Zn = 59.40 ± 22.80 μg/dL and 80.24 ± 16.04 μg/dL), respectively. The pooled SMD of Zn was significantly reduced in pre-eclamptic women as compared with normotensive pregnant women with the SMD of -1.45 (95% CI -2.26, -0.65) at 95% CI. In this review, we found that the maternal Zn serum level was significantly reduced in pre-eclamptic women than normotensive pregnant women. This suggests that Zn could be involved in the etio-pathogenesis of pre-eclampsia. However, the specific functions of Zn in pre-eclampsia pathogenesis should be proved in large-scale clinical trial studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".