Is overweight associated with periodontitis in pregnant women? Systematic review and meta-analysis
Bibliographic record
Abstract
This systematic review and meta-analysis aimed to generate pooled evidence for the association between excessive weight and pregnancy induced periodontitis. EMBASE, SCOPUS, PubMed/MEDLINE, Web of Science, BVS/LILACS, Cochrane Library and SCIELO databases were accessed. Eligibility criteria were: human clinical studies published between year 2000 and 2021. Newcastle-Ottawa scale was used to evaluate risk of bias of the studies. Meta-analysis was performed using MedCalc® Statistical Software. Eleven studies were included, evaluating 2152 pregnant women (743 with overweight/obesity and 1409 with normal body mass index - BMI), with a mean age of 29.62 years. Most studies had low risk of bias. A positive association between overweight/obesity and periodontitis was found, with an average of 61.04% of women with overweight/obesity and periodontitis, showing the overall random-effects relative risk and 95% CI of 2.21 (1.53-3.17) (p < 0.001). Arterial hypertension, gestational diabetes mellitus and excessive gestational weight gain were the most common adverse effects of maternal obesity that may have been linked to periodontitis induced pro-inflammatory state. In conclusion, a positive association was found between overweight/obesity and periodontitis during pregnancy. However, the high heterogeneity between the studies related to sample size, periodontal classification and the cutoff-points for BMI are the main limitation.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.029 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".