Effect of dental treatments on reduction of preterm birth: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Periodontal disease is associated with increased risk of preterm birth. Effective management during pregnancy may reduce preterm birth, though clinical trial evidence has been inconsistent. This study aimed to assess whether treating dental disease during pregnancy reduces preterm birth. DATA SOURCES: A systematic search was performed of EMBASE, MEDLINE, PubMed, Cochrane Library, and trial registries up to December 2023 and re-run in January 2025. STUDY ELIGIBILITY CRITERIA: Randomized controlled trials (RCTs) enrolling pregnant persons with any dental disease randomized to receive dental treatment vs no treatment were included. No language restriction was applied. STUDY APPRAISAL AND SYNTHESIS METHODS: Data were independently extracted by two researchers and assessed for risk of bias using the Cochrane Risk of Bias tool, RoB 2. A random effect meta-analysis was performed with the Mantel-Haenszel variance estimate. Certainty of evidence (COE) was assessed using GRADE. The main outcome was preterm birth (<37 weeks' gestation). Data on low birth weight were also collected. RESULTS: Fourteen RCTs (8316 participants) were included. Interventions included scaling and root planing (SRP) alone (8 RCTs), SRP with chlorhexidine mouthwash (4 RCTs), and cetylpyridinium chloride mouthwash (2 RCTs). Meta-analysis and GRADE assessment found moderate-certainty evidence suggesting that periodontal treatment results in a 15% relative risk reduction of preterm birth (risk ratio [RR] 0.85; 95% CI 0.71-1.02) compared to minimal periodontal treatment or no treatment (absolute difference 20 fewer preterm births per 1000 individuals; 95% CI from 39 fewer to 3 more). It is uncertain whether the addition of chlorhexidine mouthwash to SRP reduces preterm birth rates (RR 0.49, 95% CI 0.23-1.04, very low COE). CONCLUSION: This meta-analysis, the largest and most up-to-date on this topic, suggests that treating periodontal disease during pregnancy may reduce the risk of preterm birth. However, limitations, such as the risk of bias and variations in populations and treatment, highlight the need for well-powered RCTs with low risk of bias to evaluate the most effective dental treatment strategies. Future studies should focus on established dental disease severity and explore different dental treatment strategies, including antimicrobial mouthwash. El resumen está disponible en Español al final del artículo.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.042 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".