Assessing the association between periodontitis and premature birth: a case-control study
Bibliographic record
Abstract
BACKGROUND: Premature delivery is among the leading causes of perinatal mortality and morbidity in developed societies, which is an important obstetrics problem. Maternal periodontitis is a prevalent condition that has been suspected to be associated with adverse pregnancy outcomes such as premature birth. However, there are still conflicting results about this possible relationship, therefore this study was designed to test the association between maternal periodontitis and premature birth. This study also provides information about a new screening tool recommended for use by nurses and midwives to screen for periodontal diseases during antenatal consultations in order to improve the health of mothers and children. METHODS: A retrospective case-control study was conducted at 12 health facilities in the Southern Province of Rwanda from February to August, 2018. A total of 555 women in the postpartum period were enrolled in the study. Cases and controls were enrolled in a ratio of 1:2; each enrolled case of preterm birth was followed by 2 unmatched control subjects that were next on the register and who delivered at term gestation. A total of 185 cases of preterm deliveries and 370 controls of term delivery were enrolled in the study. Multivariate regression analysis was used and the independent variables were hierarchically entered in three groups: The first group involved demographic variables that were put in the regression model as Step 1. The second group was made up of other potential risk factors that were placed in the regression model as the second step. Periodontitis was entered in the final regression step, as it was hypothesized as the main predictor variable. RESULTS: A statistically significant association was found between periodontitis and premature birth. Women who had periodontitis had 6 times the odds of giving birth to premature birth infants compared to women who had no periodontitis (OR: 6.360, 95% CI 3.9, 10.4). CONCLUSION: The study results indicate that periodontitis is strongly associated with premature birth. Preventive solutions including the use of a periodontitis screening tool for nurses and midwives during antenatal care consultations, are highly recommended.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".