The association between periodontal disease and risk of adverse maternal or neonatal outcomes: A systematic review and meta‐analysis of analytical observational studies
Bibliographic record
Abstract
Abstract Background and Aim The aim of this meta‐analysis was to find the association between periodontal disease (PD) and the risk of adverse pregnancy outcomes, including Pre‐eclampsia (PE), premature rupture of the amniotic sac, gestational diabetes (GDM), or low birth weight (LBW) in pregnant women, which should be investigated in a systematic meta‐analysis. Methods Studies that reported the association between PD and pregnancy or neonatal outcomes and were published from January 1990 to December 2022, were identified by an extensive search in PubMed (Medline), Scopus, Web of Sciences, and Medline (Elsevier). After retrieving the studies, the screening stage was performed based on their titles, abstracts, and full texts, and after selecting the final articles, their information was extracted and their quality was assessed using the Newcastle Ottawa Scale checklist. Results Pregnant women with PD had a 1.39 higher chance of developing GDM than those who did not have the infection (risk ratio [RR]: 1.39; 95% confidence interval [CI]: 1.21−1.61; I square: 49.67%; p: 0.03). Additionally, the pooled RR of LBW was 2.19, which indicates that pregnant women with PD had a 2.19‐fold higher risk of LBW than pregnant women who do not have the infection (RR: 2.19; 95% CI: 1.82−2.64; I square: 0.00%; p: 0.65). The relationship between the risk of PE and the existence of PD was examined in 33 cohort and case‐control studies for this meta‐analysis. These results were combined, and the pooled RR was 1.43. This indicates that pregnant women with PD are 1.43 times more likely to experience PE than pregnant women without PD (RR: 1.43; 95% CI: 1.32−1.54; I square: 82.64%; p: 0.00). Conclusion According to the findings of the current meta‐analysis, PD may contribute to a higher risk of poor maternal and newborn outcomes in pregnant women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.045 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".