Risk of preeclampsia following assisted reproductive technology: systematic review and meta-analysis of 72 cohort studies
Bibliographic record
Abstract
Objective: Previous studies have indicated that women with assisted reproductive technology (ART) pregnancy have an increased risk of preeclampsia. The aim of this current study was to estimate, through a systematic review and meta-analysis of cohort studies, the risk of preeclampsia in women who conceived with ART.Materials and methods: We searched ISI Web of Knowledge, Medline/PubMed, Scopus and Embase up to August 2017 for English-language articles pertaining to risk of preeclampsia in ART pregnancy using standard keywords. Data extraction was conducted by two authors and quality of the studies was assessed using the Newcastle–Ottawa Scale. A random-effects model was used for the meta-analysis.Results: In total, 72 cohort studies (n = 164 870) were included. The results of Cochran test and I2 statistic indicated considerable heterogeneity among studies (Q = 15 415.61, df = 71, p < .001, I2=99.5%). The pooled estimate of preeclampsia risk using the random effects model was 10.8% (95% CI: 9.10–12.5). Furthermore, the funnel plot and Begg’s test showed evidence of publication bias.Conclusions: We found that the risk of preeclampsia was very high among women who conceived with ART. Women should be counseled carefully before undergoing ART treatment.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.025 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".