Effect of routine dental attendance on child oral health‐related quality of life: A cohort study
Bibliographic record
Abstract
BACKGROUND: There is an improvement in oral health status among people who receive preventive dental care during their lifetime, highlighting the possible effect in resolving oral health problems and consequently oral health-related quality of life (OHRQoL). AIM: Assessed the effect of routine dental attendance on child OHRQoL. DESIGN: This cohort study used baseline data from 639 preschoolers from 2010. After 7 years, 449 children were re-examined (70.3%). Mothers of the children completed a questionnaire collecting data on the pattern of use of dental services. Children were classified as adhering to long-term routine dental attendance according to their pattern of use (routine vs curative) in the baseline and in follow-up. The child OHRQoL was assessed through the Child Perception Questionnaire (CPQ8-10). The association between routine dental attendance and child OHRQoL was assessed using multilevel Poisson regression models. RESULTS: The proportion of participants who reported the worst CPQ8-10 scores were higher among those who, at some point in their life, experienced a curative dental attendance. Also, the mean CPQ8-10 was two times higher for non-routine dental attendance. CONCLUSION: The findings showed that there is an impact of long-term routine attendance on child OHRQoL. This is important for tackling oral health iniquities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".