Oral health‐related quality of life among women early postpartum: A cross‐sectional study
Bibliographic record
Abstract
BACKGROUND: Periodontal diseases can negatively impact the oral health-related quality of life (OHRQoL) of pregnant women. This study investigates the association between maternal oral inflammatory load (OIL), sociodemographic characteristics, and the OHRQoL in postpartum women. METHODS: In this cross-sectional study, breastfeeding mothers were recruited from St. Michael's Hospital, Toronto within 2-4 weeks postpartum. Mothers were categorized into "Normal/low" and "High" OIL groups based on the absolute counts of oral polymorphonuclear neutrophils (oPMNs). The Oral Health Impact Profile-14 questionnaire was used to assess the impact of the maternal OIL on the OHRQoL. Multiple linear regression analyses were performed to examine the association between maternal sociodemographic factors including age, marital status, education level, employment status, parity, and their OHRQoL. RESULTS: Forty-seven mothers were included in this study. Mothers with high OIL reported higher impact on their OHRQoL (30%) than mothers with normal/low OIL (21%), but these differences were not statistically different. There was a negative relationship between the mother's education level and the extent of impact of OHRQoL on the "physical pain" dimension (p < 0.05), and between the mothers' age and employment status and the "physical disability" dimension (p < 0.05). A positive correlation was noted between multi-parity and the extent of impact of OHRQoL on the "physical disability" dimension (p = 0.009), and between the marital status and the "psychological disability" dimension (p < 0.05). CONCLUSION: This study highlighted the significant impact of sociodemographic characteristics on the OHRQoL of mothers, showcasing the importance of considering these factors when implementing targeted preventive dental care programs for mothers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".