Effect of periodontal treatment in pregnancy on perinatal outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective To assess the effect of periodontal treatment during pregnancy on maternal, fetal and neonatal outcomes.Methods This is a systematic review and meta-analysis of randomized controlled trials that evaluated the effect of treatment of periodontal diseases on pregnancy outcomes. Primary outcome was perinatal mortality. Secondary outcomes were maternal and neonatal morbidity. Outcomes were pooled using fix-effect or random effects models and presented as risk ratio (RR), or mean difference (MD), and 95% confidence interval (CI).Results Twenty randomized controlled trials involving 8171 participants were included in this study. Periodontal treatment during pregnancy was associated with significantly decreased risk of perinatal mortality [N = 5942; RR = 0.53 (0.30–0.93); p = .03; heterogeneity (I2) = 0%, Number needed to treat (NNT): 162]. Periodontal treatment during pregnancy reduced risks of preterm birth [N = 7335; RR = 0.78 (0.62–0.98); p = .03; I2 = 72%, NNT = 37]. Periodontal treatment during pregnancy significantly increased birthweight (gram) [N = 4708; MD = 200.79 (63.34–337.24); p = .004; I2 = 93%]. Periodontal treatment during pregnancy was not associated with preeclampsia, gestational diabetes, cesarean section, small for gestational age, or congenital malformations.Conclusion Periodontal treatment during pregnancy reduces the risks of perinatal mortality and preterm birth, and improves birth weight.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.031 |
| 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 | 0.002 | 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".