Oral Health-Related Quality of Life during Pregnancy: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: High prevalence of Dental and periodontal problems during the gestation period may have a negative effect on oral health-related quality of life (OHRQoL) in pregnant women. This systematic review aimed to perform a quality assessment and provide a critical overview of the current research available on OHRQoL in pregnant women. METHODS: For this systemic review, all original and peer-reviewed human studies, which investigated OHRQoL of women during pregnancy or post- partum period, were searched. Studies were screened in title and abstract for the relevance by two independent investigators. Methodological quality was assessed using modified items recommended by the Newcastle-Ottawa Scale for observational studies. RESULTS: All of the eight included studies had a cross-sectional design. Meta-analysis was not possible due to the heterogeneity of key aspects among the included studies. Thus, the data from the studies were evaluated qualitatively. The overall risk of bias of the included studies was low. CONCLUSION: The main conclusion of this review is that the presence of signs and symptoms of dental and gingival disease negatively affects the self-perception of OHRQoL in pregnant women. The most affected domains of OHRQoL in pregnant women were related to mental and psychological discomfort, followed by physical and functional problems.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".