The Association between Threatened Abortion and the Risk of Autism Spectrum Disorders among Children: A Meta‐Analysis
Bibliographic record
Abstract
Background. The current study is aimed at updating the observational studies on the relationship between threatened abortion and the risk of ASD. Methods. The search keywords were covered in three electronic databases PubMed, Web of Science, and Scopus up to April 2022. The modified Newcastle–Ottawa scale (NOS) was applied to detect the quality of epidemiological studies. We used the chi‐square test and the I2 statistic to show the heterogeneity among articles. I2 more than 50% was considered high heterogeneity. Egger’s and Begg’s line regression tests were used for evaluating the publication bias. The random‐effects model was applied for the analysis of the findings. The Stata 13.0 software package was applied for analysis and indicated p value less than 0.05 as a significant level. Results. The pooled analysis reported significant differences between threatened abortion and the risk of ASD in adjusted studies (OR = 1.93; 95% CI: 1.12, 2.73; I2 = 59.5.0%) and in crude studies (OR = 2.17; 95% CI: 1.46, 2.88; I2 = 39.5%). The evidence of publication bias was not found. Conclusions. The findings suggest that threatened abortion is a risk factor for ASD. As a result, screening tools to detect are necessary in mothers facing a threatened abortion.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.035 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".