The Association between Assisted Reproductive Technology and the Risk of Autism Spectrum Disorders among Offspring: A Meta-analysis
Bibliographic record
Abstract
BACKGROUND: This review aimed to determine the association between assisted reproductive technology (ART) and increased chances of having an autistic child. METHODS: The Web of Science, PubMed, and Scopus databases were systematically searched for studies published until December 2020 with the restricted English language. The Newcastle-Ottawa Scale (NOS) for cohort and case-control studies has been used for the evaluation of quality in individual studies. We evaluated the heterogeneity among the studies using I-squared. Publication bias was assessed using the funnel plot and Egger's and Begg's tests. We presented results using odds ratio (OR) and relative ratio (RR) estimates with its 95% confidence intervals (CI) using a randomeffects model. RESULTS: In total, 18 articles were included in the present study. The overall findings of the present meta-analysis show that the use of ART didn't associate with the risk of autism spectrum disorders (ASD) among offspring based on OR and RR (OR = 1.04, 95% CI: 0.88-1.21) and (RR = 1 .26, 95% CI: 0.96- 1 .55), respectively. We showed a significant association between ART and the risk of ASD in Asia than in the other regions without heterogeneity. CONCLUSION: Our result showed that the risk of ASD was not increased in children born from ART. Possible interaction between ART and other regions with increased risk of ASD is important to point and future studies of this topic were recommended.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.049 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".