Impact of Maternal Periodontitis on Preterm Birth and Low Birth Weight in Babies: Results of a Scoping Review
Bibliographic record
Abstract
Background Periodontitis has been documented as public health concern but its association with preterm and low birth weight remains uncertain, thus the objective of this scoping review is to summarize the most recent published evidence related to the impact of periodontitis on preterm birth and low birth weight in order to improve public awareness and to inform policies for oral health during pregnancy. Methods Hinari, PubMed, and Google Scholar were searched to acquire the published literature. The retrieved studies included cross-sectional, case control studies and randomized controlled trials with available full text published in English from 2008 to 2019. Results After combining the key words, 333 articles were identified with only 133 eligible articles published from 2008 to 2019. After reviewing the available 50 full text articles, duplicates were removed and 15 studies fully met the inclusion criteria. There were 13 articles that supported the association between maternal periodontitis and preterm low birth weight while 2 found no evidence to support the association. Conclusion The results of this scoping review contribute to an increasing body of evidence to support the hypothesis that maternal periodontal disease may be a risk factor for preterm delivery and low birth weight. Rwanda J Med Health Sci 2020;3(3):372-386
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".