Neonatal Adverse Outcomes of Induction and Expectant Management in Fetal Growth Restriction: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background and objective: Fetal growth restriction (FGR) is a pathological condition in which the fetus can not reach its expected growth potential. When it is diagnosed as suspected FGR, it remains an unsolved problem whether to direct induction or continue expectant management. In order to effectively reduce the incidence of neonatal adverse outcomes, we aimed to evaluate the appropriate method in which could have lower incidence of neonatal adverse outcomes. Methods: We searched relevant literatures through PubMed, Web of Science and Cochrane Library from inception to Jan. 10, 2020. We defined induction as experimental group and expectant management as control group. Pooled odds ratios (OR) with 95% confidence intervals (CI) was calculated using random-effects models owing to the heterogeneous. Furthermore, we conducted a sensitivity analysis to explore the robustness of included literature. We use the Newcastle-Ottawa scale (NOS) to evaluate available studies quality. And we applied the funnel plot to describe the publication bias. Also according to the study method, sample size, area, and NOS score to perform a subgroup analysis to further evaluate the significance between induction and expectant management. Results: Our study included a total of 8 articles with 6706 patients, which consisted of 4 randomized controlled trial (RCT), 3 retrospective cohort studies and 1 prospective cohort study. The total pooled OR and 95% CI between the induction group and the expected management group was 1.38 (95% CI, 0.84-2.28) in the random model. The heterogeneity is I2 = 84%, P < 0.01. Sensitivity analysis showed that omitting any one of these studies, the neonatal adverse outcomes in induction vs expectant management still present similar. The funnel plot and linear regression equation showed that was no publication bias in our study (P = 0.75). Subgroup analysis showed that no significant difference was observed in study method, sample size, area, and NOS score (all P > 0.05). Conclusion: Regardless of whether induction or expectant management of FGR, the neonatal adverse outcomes showed no obvious difference. More studies are supposed to conduct, and confounding factors are needed to take into consideration to elucidate the significance of two measures on suspected FGR.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".