EFFECT OF MULTIDISCIPLINARY CARE ON NEONATAL OUTCOMES IN HIGH-RISK PREGNANCIES. A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: High-risk pregnancies are associated with elevated neonatal complications, including preterm birth, low birth weight, and increased neonatal intensive care unit (NICU) admissions. Although traditional obstetric care focuses on maternal monitoring, growing evidence suggests that multidisciplinary care (MDC)—which involves coordinated management by various healthcare specialists—may significantly enhance neonatal outcomes. However, a consolidated analysis of MDC’s impact on neonatal health across different high-risk populations remains limited. Objective: This systematic review aims to evaluate the effectiveness of multidisciplinary care in reducing neonatal complications among women with high-risk pregnancies compared to conventional care models. Methods: A systematic review was conducted following PRISMA guidelines. Electronic databases including PubMed, Scopus, Web of Science, and Cochrane Library were searched for studies published between 2018 and 2024. Keywords included “Multidisciplinary Care,” “High-Risk Pregnancy,” and “Neonatal Outcome.” Eligible studies included randomized controlled trials, cohort studies, and observational studies evaluating neonatal outcomes in the context of MDC. Data extraction and risk of bias assessments were independently performed by two reviewers using standardized tools such as the Cochrane Risk of Bias Tool and the Newcastle-Ottawa Scale. Results: Eight studies involving over 2,000 high-risk pregnancies were included. MDC was associated with reduced rates of miscarriage, fetal death, neonatal hypoglycemia, macrosomia, and NICU admissions. Several studies reported statistically significant improvements, including a reduction in adverse neonatal outcomes from 60.8% to 3.5% (p<0.001), and improved immunologic and anthropometric neonatal profiles. Overall, MDC models demonstrated superior outcomes compared to routine obstetric care. Conclusion: Multidisciplinary care significantly improves neonatal outcomes in high-risk pregnancies by offering coordinated, patient-centered interventions. Despite promising findings, limitations such as heterogeneity in study designs and sample sizes underscore the need for further large-scale, high-quality trials to refine care protocols and validate effectiveness across broader populations.
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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".