Impacts of assisted reproductive technology on autism spectrum disorders in offspring: a Meta-analysis
Bibliographic record
Abstract
Objective To systematiclly evaluate the relationship between assisted reproductive technology（ART） and autism spectrum disorders（ASD） in their offspring. Methods Systematic searches of PubMed, Web of Science, CBM, VIP, and CNKI databases were conducted to collect the cohort studies on ASD in ART progeny published from database establishment to April 1, 2022. After four independent reviewers screened the literature, extracted the data and assessed their quality with Newcastle-Ottawa scale（NOS）, a Meta-analysis was performed using Stata 15.1 software. Results Finally, 8 articles were included to assess the risk of ASD in 335507 offspring after ART（ART group） and 14233258 offspring of natural conception（NC group）. After excluding one study with greater heterogeneity by sensitivity analysis,the incidence of child with ASD in ART group was significantly higher than that in NC group（RR=1.06, 95%CI1.01-1.11, P<0.05）. The stratified analysis showed that the risk of ASD in ART offspring was higher than that in NC offspring based on the subgroup analysis on the data from Europe and the United States（RR=1.06, 95%CI 1.01-1.11, P=0.014）, Newcastle Ottawa scale（NOS） score 7-9（RR=1.06, 95%CI 1.01-1.11, P=0.022）, ART sample size of more than 10000 participants（RR=1.06, 95%CI 1.01-1.11, P=0.016）, correction factors of more than 5（RR=1.06, 95%CI 1.01-1.11, P=0.014） and corrected maternal psychiatric morbidity（RR=1.06, 95%CI 1.01-1.11 , P=0.022）. Conclusions Current evidence suggests that ART may increase the risk of autism in offspring, but more rigorous cohort studies need to be designed to increase the strength of the evidence.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.047 |
| Bibliometrics | 0.005 | 0.005 |
| 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".