Association between maternal lipid levels during pregnancy and delivery of small for gestational age: A systematic review and meta-analysis
Bibliographic record
Abstract
Background Studies investigating the relationship between gestational dyslipidemia and small for gestational age (SGA) have reported differing results. This review was performed to determine whether maternal lipid levels during pregnancy were associated with SGA. Methods Literature searches for relevant studies were conducted systematically from establishment until February 2022 with PubMed, Embase, Cochrane Library and Web of Science. Risk of bias was assessed with the Newcastle-Ottawa Scale and 11-item checklist. According to the classification of GHD parameters, meta-analyses reporting cases regarding total cholesterol (TC), triglycerides (TG), low-density lipoprotein-cholesterol (LDL-C) and high-density lipoprotein-cholesterol (HDL-C) were performed respectively. If I2≥ 50%, considered to demonstrate substantial heterogeneity, the random effect model was employed. Otherwise, a fixed effect model was employed. Results Eight studies (14,213 pregnancies) were included. Decreased levels of TC (MD −0.13; 95% CI −0.24 to −0.02), TG (MD −0.09; 95% CI −0.14 to −0.03) and LDL-C (MD −0.12; 95% CI −0.23 to −0.00) were risk factors for SGA infant birth. No evident association was observed between HDL-C and delivery of SGA (MD −0.08; 95% CI −0.19 to 0.02). Conclusion Gestations complicated with dyslipidemia, especially lower concentrations of TC, TG and LDL-C, were at significantly higher risk of delivery of SGA. Systematic review registration [ www.crd.york.ac.uk/prospero ], identifier [CRD42022304648].
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.030 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| 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".