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 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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.043 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".