Tooth Erosion with Low Severity Does Not Impact Child Oral Health-Related Quality of Life
Bibliographic record
Abstract
BACKGROUND: Prevalence data about tooth erosion has attracted increasing attention in the dental community; however, no study has addressed the impact of this condition on child oral health-related quality of life (COHRQoL). This study assessed the impact of tooth erosion on COHRQoL. METHODS: This study followed a cross-sectional design, with a multistage random sample of 944 11- to 14-year-old children representative of Santa Maria, a southern city in Brazil. They were examined for recording the prevalence and severity of tooth erosion by 2 examiners. Children completed the Brazilian version of Child Perceptions Questionnaire (CPQ(11-14)) and data about socioeconomic variables of the target population were collected by means of a structured questionnaire. The Poisson regression model using robust variance was performed to assess the association between the predictor variables and the outcomes. RESULTS: Prevalence of tooth erosion (7.2%) and severity were low. Poisson regression models showed a distinct gradient in mean CPQ(11-14) scores by socioeconomic indicators. Children with tooth erosion with low levels of severity did not report higher means in the total scores or domains of CPQ(11-14). CONCLUSION: The presence of tooth erosion of low severity did not have a significant negative impact on the children's perception of oral health or on their daily performance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".