Correlation Between West Nile Virus and Pregnancy: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: West Nile Virus [WNV] is a mosquito-borne flavivirus. It has spread globally, causing asymptomatic to severe neurological diseases in humans, with an increased risk in older adults and those with underlying conditions. This review examines WNV's impact on pregnancy, focusing on maternal and neonatal symptoms and risks. METHODS: This systematic review included primary studies from "PUBMED" and "SCOPUS" databases, as well as Google and Google Scholar, conducted in July 2024 using the appropriate keywords. This review adhered to PRISMA guidelines and utilized the Newcastle-Ottawa scale for bias assessment. RESULTS: Seven primary studies were included in the systematic review. Fever was the predominating symptom, including neurological manifestations, respiratory symptoms, myalgia, weakness, nausea, vomiting, and rashes. Delivery, in most cases, progressed without any complications, while no infection was noted. Most of the neonates had a normal Apgar score, and their developmental functions did not seem to be affected. Even though, antibodies against WNV were detected in breast milk, no association with transmission to the neonate was observed. CONCLUSIONS: WNV infection is mostly associated with favorable outcomes during pregnancy. However, larger cohorts are needed to confirm our conclusions. Prompt diagnosis and public health surveillance are pivotal to eliminate disease transmission.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".