COVID-19 and Pregnancy: Citation Network Analysis and Evidence Synthesis
Bibliographic record
Abstract
BACKGROUND: COVID-19 spread quickly around the world shortly after the first outbreaks of the new coronavirus disease at the end of December 2019, affecting all populations, including pregnant women. OBJECTIVE: The aim of this study was to analyze the relationship between different publications on COVID-19 in pregnancy and their authors through citation networks, as well as to identify the research areas and to determine the publication that has been the most highly cited. METHODS: The search for publications was carried out through the Web of Science database using terms such as "pregnancy," "SARS-CoV-2," "pregnant," and "COVID-19" for the period between January and December 2020. Citation Network Explorer software was used for publication analysis and VOSviewer software was used to construct the figures. This approach enabled an in-depth network analysis to visualize the connections between the related elements and explain their network structure. RESULTS: A total of 1330 publications and 5531 citation networks were identified in the search, with July being the month with the largest number of publications, and the United States, China, and England as the countries with the greatest number of publications. The most cited publication was "Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: a retrospective review of medical records" by Chen and colleagues, which was published in March 2020. Six groups identified as being close in the citation network reflect multidisciplinary research, including clinical characteristics and outcomes in pregnancy, vertical transmission, delivery mode, and psychological impacts of the pandemic on pregnant women. CONCLUSIONS: Thousands of articles on COVID-19 have been published in several journals since the disease first emerged. Identifying relevant publications and obtaining a global view of the main papers published on COVID-19 and pregnancy can lead to a better understanding of the topic. With the accumulation of scientific knowledge, we now know that the clinical features of COVID-19 during pregnancy are generally similar to those of infected nonpregnant women. There is a small increase in frequency of preterm birth and cesarean birth, related to severe maternal illness. Vaccination for all pregnant women is recommended. Several agents are being evaluated for the treatment of COVID-19, but with minimal or no information on safety in pregnancy. These results could form the basis for further research. Future bibliometric and scientometric studies on COVID-19 should provide updated information to analyze other relevant indicators in this field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".