Do Oral Health Conditions Adversely Impact Young Adults?
Bibliographic record
Abstract
This study assessed the extent to which clinically measured oral health conditions, adjusted for sociodemographic and oral health behavior determinants, impact adversely on the oral health-related quality of life (OHRQoL) in a sample of Belgian young adults. The null hypothesis was that, among young adults, the oral health conditions would have no impact on their quality of life. The participants were 611 new patients aged 16-32 years seeking consultation at the Saint-Luc University Hospital in Brussels in 2010-2011. The patients (56.0% female) were examined for their oral health conditions and answered a validated questionnaire about sociodemographic and oral health behavior determinants in addition to questions about their OHRQoL. The abridged Oral Health Impact Profile-14 was used to assess the OHRQoL. Interexaminer reliability for caries was 0.86 (95% CI 0.84-0.89, nonweighted κ). The outcome was a high score on the OHRQoL (median split). Hierarchical logistic regression analysis showed that young adults with clinical absolute D1MFS scores between 9 and 16 (OR = 2.14, p = 0.031) and between 17 and 24 (OR = 3.10, p = 0.003) were significantly more likely to report a high impact on their quality of life than those with lower scores. Also, periodontal conditions compromised significantly (OR = 1.79, p = 0.011) the quality of life of young adults. In conclusion, this study identified oral health conditions with a significant adverse effect on the OHRQoL of young adults. However, the prevalence of young adults reporting impacts on at least 1 performance affected fairly often or very often was limited to 18.7% of the sample.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".