Prevalence of Periodontitis and Associated Factors among Pregnant Women: A cross sectional survey in Southern Province, Rwanda
Bibliographic record
Abstract
Background: The literature has shown the relationship between maternal periodontitis and complications associated with pregnancy. Thus, prevalence estimates and risk factor identification for periodontitis during pregnancy in Rwanda are paramount. Aim: The aim of the current study was to determine the prevalence of periodontitis and identify related risk factors among pregnant women in Rwanda. Methods: A cross sectional study was conducted to determine the prevalence of periodontal diseases in a convenience sample of 400 pregnant women in the Southern Province of Rwanda. A logistic regression analysis using a hierarchical approach was performed to assess the risk factors for periodontal disease. Socio demographic factors were put in the regression model first followed by a second step for other potential factors. Results: The overall prevalence of periodontitis was 60.5%. Multivariable logistic regression showed that age OR=2.48 (95% CI. 1.18-5.22), education level OR=82.15 (95% CI. 8.21-822.11), socio economic status OR=2.28 (95% CI. 1.49-6.62), employment status OR=7.3 (95% CI. 1.38-38.74, and tobacco use OR=6.89 (95% CI. 1.78-60.65) were significantly associated with periodontitis. Conclusion: Periodontitis appears to be a common problem among pregnant women in Rwanda. Risk factor screening could help identify pregnant women at higher risk of periodontal disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".