Influencing factors of neural tube malformation: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Neural tube malformation is a common congenital malformation and its influencing factors were still unclear. This paper aims to explore the main influencing factors of neural tube malformation, and provide reference for the primary prevention of neural tube malformation. Methodology: Case-control literatures on the influencing factors of neural tube malformation from 1990 to 2021 were searched from Chinese and English websites. The quality of the included literatures was evaluated according to Newcastle-Ottawa Scale (NOS) scale and data were extracted. Meta-analysis was performed on the data using funnel plot and Egger's est evaluated publication bias, and sensitivity analysis was performed by eliminating individual studies one by one. Results: A total of 49 case-control studies were included. Meta-analysis showed that the main influencing factors of neural tube malformation were folic acid (odds ratio (OR)OR=0.31, 95%CI: 0.20-0.47), fever (OR=3.02, 95%CI: 2.38-3.83), obesity (OR=1.76, 95%CI: 1.39-2.21), passive smoking (OR=1.91, 95%CI: 1.52-2.40). Antiepileptic drugs (OR=6.10, 95%CI: 2.58-14.43); Heavy metals (Zinc: OR=2.37, 95%CI: 1.06-5.30, mercury: OR= 4.61, 95%CI: 2.85-7.47). Conclusion: Prenatal supplementation with folic acid and zinc has been shown to reduce the risks of neural tube defects. It is recommended that women of childbearing age take folic acid and zinc supplements before and during pregnancy. Other factors such as fever, obesity, passive smoking, antiepileptic drugs, and mercury exposure have been associated with an increased incidence of neural tube abnormalities. Neurological tube abnormalities can be reduced by folic acid and zinc, which act as protective factors. The incidence of neural tube abnormalities is increased by fever, obesity, passive smoking, antiepileptic drugs, and mercury.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".