Oral Health Knowledge of Pregnant Women on Pregnancy Gingivitis and Children's Oral Health
Bibliographic record
Abstract
OBJECTIVE: Pregnancy gingivitis and early childhood caries remain prevalent in Hong Kong. The aim of this study was to assess pregnant women's knowledge and beliefs related to pregnancy gingivitis and children's oral health. STUDY DESIGN: An outreach survey was carried out in a clinic that provided antenatal examination. A written oral health questionnaire related to pregnancy gingivitis and early childhood caries was administered to pregnant women. Of the 106 pregnant women who enrolled in the study, 100 completed the questionnaires. RESULTS: Among the 100 subjects, only 39% correctly identified that hormonal changes contribute to pregnancy gingivitis. Only 36% identified red and swollen gums as signs of gingivitis. Furthermore, 53% of the surveyed pregnant women were not sure about the amount of toothpaste to administer to a child aged 18 months to 5 years. Almost 50% assumed that a replanted avulsed tooth would probably not survive within a short extra-alveolar period of less than 60 minutes. CONCLUSION: Prenatal women generally lack knowledge of a common oral disease that occurs during pregnancy and of what constitutes adequate oral health care for children. Oral health care education should be implemented as part of a prenatal care program.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".