Assessing the association between periodontitis and preterm low birth weight: A case control study
Bibliographic record
Abstract
Abstract Background Premature deliveries are the main causes of prenatal and infant mortality and morbidity in developed societies and is an important problem in obstetrics. Maternal periodontitis is a very prevalent condition that has suspected to be associated with adverse pregnancy outcomes like preterm birth and low birth weight. However, there are still conflicting results and this study have been done to determine the association between periodontitis and preterm low birth weight in order to get necessary information that will enable us to improve mothers’ and children’s health by recommending the screening tool to be used by nurses and midwives to screen for periodontal diseases during antenatal consultations. Methods A case control study was done on 555 women on post-partum period. This case control was in ratio of 1:2; 1 case of preterm and low birth weight to 2 controls. There were 185 cases with preterm deliveries/ gestation age < 37 weeks and low birth weight / weight < 2500 g and 370 controls with term delivery/ gestation age of above or equal to 37 weeks and normal birth weight babies 2500 g and above. Multivariate regression analysis was done and the variables were hierarchically grouped into three groups: first categories of demographic variables were put in the regression model as step 1. Second category were other potential factors were put in regression model as the second step. The third category or the third step of regression model, the researcher put periodontitis as it was hypothesized a major predictor variable. Results Significant association was found between periodontitis and preterm low birth weight; women who had periodontitis had 6 times the odds of giving birth to preterm low birth weight babies compared to women who had no periodontitis (p < 0.001) (95% CI 3.9, 10.4). Conclusion Periodontitis is a risk factor for preterm low birth weight and preventive solutions like having a periodontitis screening tool for nurses and midwives during antenatal care consultations are highly recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".