Excess body weight and periodontitis in pregnant woman: a systematic review with meta-analysis
Bibliographic record
Abstract
Objective: to evaluate the evidence of the association between excess weight and periodontitis in pregnant women. Material and Methods: a systematic review was carried out using following databases (MedLine/PubMed, Lilacs, SciELO, Embase, and Web of Science) until April 30, 2024. Studies with varying epidemiological designs, at least 100 participants, without restriction with regards to the date of publication or language, comprised the eligibility criteria. Three independent reviewers performed the selection of articles and data extraction. Proportion and random effects meta-analysis were performed to calculate the measurements. Newcastle-Ottawa Quality Assessment Scale, Grading of Recommendations, Assessment, Development and Evaluations – GRADE tool, funnel, and Galbraith graphs assessed the risk of bias. Results: 1.208 records were identified in the electronic and manual database searches, with only 6 studies met the eligibility criteria for the meta-analysis. Totaling a sample of 4,077 pregnant women. Gestational excess weight was associated with periodontitis the random effects model, ORadjusted: 2.00 (95% CI: 1.17 – 2.83), with 96.0% I². Publication bias was a limitation. Conclusion: the main findings validated the association between excess weight and periodontitis in pregnant women. Both the exposure and the outcome impact not only on pregnant women, but also on infant morbidity and mortality rates. PROSPERO: CRD42018100868.
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 | 0.017 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.035 |
| 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) | 0.004 | 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".