Does having children affect women's oral health? A longitudinal study
Bibliographic record
Abstract
BACKGROUND: Many believe women's oral health deteriorates as a result of having children. If so, such associations should exist among women but not among men. The aims of this study were to investigate whether number of children is associated with experience of dental disease and tooth loss among both men and women and to examine whether this association is affected by other variables of interest. METHODS: This study used data from the Dunedin Multidisciplinary Health and Development study, a longitudinal study of 1037 individuals (48.4% female) born from April 1972 to March 1973 in Dunedin, New Zealand, who have been examined repeatedly from birth to age 45 years. RESULTS: Data were available for 437 women and 431 men. Those with low educational attainment were more likely to have more children and began having children earlier in life. Having more children was associated with experiencing more dental caries and tooth loss by age 45, but this association was dependent on the age at which the children were had. Those entering parenthood earlier in life (by age 26) had poorer dental health than those entering parenthood later in life, or those without children. There was no association between number of children and periodontal attachment loss (PAL). Low educational attainment, poor plaque control, never routine dental attendance, and smoking (for PAL) were associated with PAL, caries experience, and tooth loss. CONCLUSIONS: Social factors associated with both the timing of reproductive patterns and health behaviors influence the risk of dental disease and its management.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".