Risk of preterm birth in maternal influenza or SARS-CoV-2 infection: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Influenza is a major threat to global health and is an important cause of respiratory diseases. However, there was a controversy on the impacts of influenza infection on adverse pregnancy outcomes and the infant's health. This meta-analysis aimed to investigate the impact of maternal influenza infection on preterm birth. Methods: Five databases, including PubMed, Embase, Cochrane Library, Web of Science, and China National Knowledge Infrastructure (CNKI) were searched for eligible studies on December 29, 2022. The Newcastle-Ottawa Scale (NOS) was used to assess the included quality of the included studies. As for the incidence of preterm birth, odds ratios (OR) and 95% confidence intervals (CIs) were pooled, and the results of the current meta-analysis were displayed in forest plots. Subgroup analyses based on similarity in different aspects were conducted for further analysis. A funnel plot was used to assess the publication bias. All of the above data analyses were performed using STATA SE 16.0 software. Results: =0.00%, P<0.1) in pregnancy were at an increased risk of preterm birth, while those infected with influenza A alone or seasonal influenza were not (P>0.1). Conclusions: Women should take active steps to avoid influenza infection during pregnancy, especially influenza A and B and SARS-CoV-2, to reduce the possibility of preterm birth.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| 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".