Relationship between Vaginal Bacterial Infections and Pregnancy Outcomes: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Bacterial infections are among the most serious infections worldwide. They can cause miscarriage, premature birth, stillbirth, and ectopic pregnancy in pregnant women. The aim of this study was to investigate the relationship between bacterial infections and pregnancy outcomes through a systematic review and meta-analysis. Materials and Methods: PubMed, Scopus, Web of Science, and Embase databases were searched from January 2000 to December 2018 using appropriate keywords to identify related articles. The final related studies were selected and evaluated using the Newcastle-Ottawa Scale (NOS). Results: Results of this meta-analysis based on combining case-control studies showed that the presence of bacterial infections could lead increase in the odds of all pregnancy outcomes like premature infant birth (odd ratio [OR]: 1.50; 95% Confidence Interval [CI], 1.39-1.61), preterm delivery (OR: 1.54; 95% CI, 1.39-1.70), abortion (OR: 1.16; 95% CI, 1.04-1.29), stillbirth (OR, 1.29; 95% CI, 1.12-1.49), and ectopic pregnancy (OR: 1.12; 95% CI, 1.05--1.19). The results showed that the Risk Ratio (RR) of preterm delivery in pregnant women with vaginal infections was 1.57 (95% CI, 1.46-1.67), whereas the RR of abortion was 2.02 (95% CI, 1.72-2.38). Conclusions: Based on the results of this meta-analysis, the presence of bacterial infections in pregnant women can lead increase in the risk of pregnancy outcomes especially, preterm delivery, abortion, stillbirth, and ectopic pregnancy. Therefore, it is necessary for obstetricians and gynecologists to pay attention to the diagnosis of these infections in women before pregnancy and during pregnancy in order to prevent the consequences of these infections.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.047 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".