Poor oral health‐related quality of life among pregnant women: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: The aim of this systematic review was to assess the impacts of oral health determinants (eg frequency of brushing < 2 day, poor life style and non-white ethnicity) and clinical oral caries indices (eg Decayed, Missing, and Filled Teeth (DMFT)) and periodontal disease) on Poor Quality of Life (PQoL) among PW. METHODS: The search strategy was restricted to publications in English before 1 December 2019 in the PsycINFO, PubMed, SciELO, Scopus and Web of Science databases. We only included oral diseases considered as public health issues with a global burden. As a result, investigations reporting the frequency of brushing, poor lifestyle behaviours, non-white ethnicity, DMFT scores and periodontal disease as an outcome were included. The two reviewers resolved any disagreements. Reviewers analysed the full texts, considering the inclusion and exclusion criteria. Also, a manual search of the reference lists was performed on all the selected studies. RESULTS: In total, 11 publications were included in the meta-analysis. Findings indicate a positive association between non-white ethnicity and PQoL among PW. Among PW, those who had non-white ethnicity were 1.43 times more likely to have PQoL (OR = 1.43, 95% CI = 1.17, 1.70). A positive association between DMFT and poor QoL among PW was also observed. Those who has suffered DMFT were 1.4 times more likely to have poor QoL (OR = 1.4, 95% CI = 1.24, 1.55). CONCLUSIONS: Results from this meta-analysis support the need for behavioural interventions for improving oral hygiene in expectant mothers. This could help to decrease periodontal conditions and improve their oral and general life quality.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.007 | 0.007 |
| 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".