The relationship between cadmium exposure and preeclampsia: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Cadmium (Cd) is a heavy metal associated with several human disorders. Preeclampsia is a major cause of maternal mortality worldwide. The association between maternal Cd exposure and preeclampsia remains elusive. Methods: To better understand this relationship, we conducted a systematic review and meta-analysis of eligible studies from five databases (PubMed, Embase, Web of Science, Scopus, and CNKI) from their inception to September 10, 2022. The quality of these studies was evaluated using the Newcastle-Ottawa quality assessment scale (NOS). We use random-effects models to calculate overall standardized mean differences (SMDs) and 95% confidence intervals (CIs). Sensitivity analyses were performed to assess the robustness of our results. We also evaluated publication bias using Egger's and Begg's tests. Additionally, we conducted meta-regression and sub-group analyses to identify potential sources of heterogeneity between studies. Results: < 0.01). No significant publication bias was detected in Begg's or Egger's tests. Meta-regression suggested that geographical location, year of publication, cadmium samples, sample size, and measurement methods did not contribute to heterogeneity between studies. Conclusion: Our findings suggest that maternal blood cadmium levels are associated with an increased risk of preeclampsia. In contrast, the pregnant women's urine or placental levels of cadmium may not suggest preeclamptic risk during pregnancy. Further high-quality clinical studies and animal experiments are needed to understand this association better. Systematic review registration: PROSPERO, https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=361291, identifier: CRD42022361291.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".