The Use of Artificial Intelligence in Pre-eclampsia: Umbrella Review Protocol
Bibliographic record
Abstract
RationalePre-eclampsia, a severe hypertensive disorder of pregnancy, poses significant maternal and perinatal risks. Artificial intelligence (AI) offers the potential for improved prediction, risk stratification, and personalized management. This umbrella review aims to synthesize existing systematic reviews to evaluate AI’s current applications, benefits, limitations, and ethical considerations in pre-eclampsia care. MethodsThis umbrella review will follow the Joanna Briggs Institute (JBI) methodology and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We will systematically search major databases for relevant systematic reviews that examine the application of AI in pre-eclampsia. Data extraction will include information on AI algorithm performance, clinical applications, predictive variables, population diversity, ethical considerations, and limitations. Quantitative and qualitative synthesis of the extracted data will be performed to address the specific aims. Discussion This review’s findings will critically examine AI’s translational potential in pre-eclampsia care. We will discuss the balance between the promise of enhanced predictive accuracy and the practical challenges of clinical implementation, including data quality, model interpretability, and the need for rigorous validation across diverse populations. Ultimately, this review will contribute to a nuanced understanding of how AI can be responsibly leveraged to improve maternal and perinatal outcomes in pre-eclampsia.
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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 | 0.083 | 0.121 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.089 | 0.014 |
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".