Meta-Analysis of Predictive Modelling Approaches and Systematic Reviews for Maternal Healthcare Outcomes
Bibliographic record
Abstract
Enhancing parental care is of utmost importance in ensuring the well-being of pregnant women throughout theirpregnancy and childbirth journey. Although there have been significant advancements in this area, persistent challenges such as infections, hemorrhage, hypertension, unsafe abortions, and other concerns remain. Prioritizing maternal health could greatly reduce mortality rates and promote safer pregnancies. This meta-analysis assesses research methodologies in maternal healthcare outcomes, evaluating their strengths and weaknesses. We also explore the prevalence of systematic reviews in maternal health to enhance healthcare outcomes. We examined five major databases—Google Scholar, PubMed, Elsevier, PLOS, and BMC—encompassing descriptive and computational research on maternal outcomes between 2000 and 2021. Our search terms included predicting, modeling, maternal, outcome, healthcare forecasting, demonstrating, consequence, diagnosis, machine learning, mathematical, and statistical. Forty-four papers related to maternal outcomes were reviewed. Google Scholar yielded 50 articles (46.30%), PubMed 33 articles (31.48%), Elsevier 12 articles (11.11%), BMC nine articles (9.26%), and PLOS two articles (1.85%). Our findings highlight a high awareness of maternal outcome prevalence. Multiple factors contribute to maternal risk, including maternal education, economic circumstances, financial constraints, and access to antenatalcare. Therefore, this work advocates for the adoption of additional methods and mathematical models to predictmaternal outcomes, ultimately improving maternal healthcare.
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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 | 0.053 | 0.167 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.057 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".