Fetal aortic isthmus Doppler assessment to predict the adverse perinatal outcomes associated with fetal growth restriction: systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Purpose Fetal growth restriction (FGR) management and delivery planning is based on a multimodal approach. This meta-analysis aimed to evaluate the prognostic accuracies of the aortic isthmus Doppler to predict adverse perinatal outcomes in singleton pregnancies with FGR. Methods PubMed, EMBASE, the Cochrane Library, ClinicalTrials.gov and Google scholar were searched from inception to May 2021, for studies on the prognostic accuracy of anterograde aortic isthmus flow compared with retrograde aortic isthmus flow in singleton pregnancy with FGR. The meta-analysis was registered on PROSPERO and was assessed according to PRISMA and Newcastle–Ottawa Scale. DerSimonian and Laird’s random-effect model was used for relative risks, Freeman-Tukey Double Arcsine for pooled estimates and exact method to stabilize variances and CIs. Heterogeneity was quantified using I2 statistics. Results A total of 2933 articles were identified through the electronic search, of which 6 studies (involving 240 women) were included. The quality evaluation of studies revealed an overall acceptable score for study group selection and comparability and substantial heterogeneity. The risk of perinatal death was significantly greater in fetuses with retrograde Aortic Isthmus blood flow, with a RR of 5.17 (p value 0.00001). Similarly, the stillbirth rate was found to have a RR of 5.39 (p value 0.00001). Respiratory distress syndrome had a RR of 2.64 (p value = 0.03) in the group of fetuses with retrograde Aortic Isthmus blood flow. Conclusion Aortic Isthmus Doppler study may add information for FGR management. However, additional clinical trial are required to assess its applicability in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".