Treatment of Periodontal Disease and Prevention of Preterm Birth: Systematic Review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: There is a controversy regarding the benefits of periodontal treatment during pregnancy. We aimed to evaluate its effect on the risk of preterm birth and to explore the heterogeneity between studies. STUDY DESIGN: A systematic review and meta-analysis of randomized controlled trials were performed. Studies in which women were randomized for periodontal treatment versus no treatment were included. Pooled risk ratios (RRs) with their 95% confidence intervals (CIs) were calculated using random-effect models. A sensitivity analysis was performed. RESULTS: Twelve randomized trials were included in the meta-analysis. Pooled estimates showed no significant reduction of preterm birth with periodontal treatment (RR: 0.89; 95% CI: 0.73 to 1.08). However, the substantial heterogeneity among studies (I2 = 52%) could be explained either by the risk of bias, the level of income, or by the use of chlorhexidine mouthwashes as a cointervention. Daily use of chlorhexidine mouthwash was associated with a reduction of preterm birth (RR: 0.69; 95% CI 0.50 to 0.95), with moderate heterogeneity among the five studies included (I2 = 43%). CONCLUSION: There is an important heterogeneity between randomized trials that evaluated the effect of periodontal treatment on the risk of preterm birth. Chlorhexidine mouthwash as a preventive agent should be further evaluated.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.022 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".