Impact of tooth loss related to number and position on oral health quality of life among adults
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to evaluate the impact of tooth loss on oral health-related quality of life (OHRQoL) in adults with emphasis on the number of teeth lost and their relative position in the mouth. METHODS: The study population was a cross-sectional household probability sample of 248, representing 149,635 20-64 year-old residents in Piracicaba-SP, Brazil. OHRQoL was measured using the OHIP-14. Socioeconomic, demographic, health literacy, dental services use data and clinical variables were collected. Oral examinations were performed using WHO criteria for caries diagnosis, using the DMFT index; that is, the sum of decayed, missing and filled teeth (DMFT). An ordinal scale for tooth loss, based on position and number of missing teeth, was the main explanatory variable. The total OHIP score was the outcome for negative binomial regression and OHIP prevalence was the outcome for logistic regression at 5% level. A hierarchical modeling approach was adopted according to conceptual model. RESULTS: OHIP score was 10.21 (SE 1.16) with 48.1% (n=115) reporting one or more impacts fairly/very often (OHIP prevalence). Significant prevalence rate ratios (PRRs) for OHIP severity were observed for those who had lost up to 12 teeth, including one or more anterior teeth (PRR=1.63, 95%CI 1.06-2.51), those who had lost 13-31 teeth (PRR=2.33, 95%CI 1.49-3.63), and the edentulous (PRR=2.66, 95%CI 1.55-4.57) compared with fully dentate adults. Other significant indicators included those who only sought dental care because of dental pain (PRR=1.67, 95%CI 1.11-2.51) or dental needs (PRR=1.84, 95%CI 1.24-2.71) and having untreated caries (PRR=1.57 95%CI 1.09-2.26). Tooth loss was not significantly associated with OHIP prevalence; instead using dental services due to dental pain (PR=2.43, 95%CI 1.01-5.82), having untreated caries (PR=3.96, 95%CI 1.85-8.51) and low income (PR=2.80, 95%CI 1.26-6.42) were significant risk indicators for reporting OHIP prevalence. CONCLUSION: Our analyses showed OHRQoL gradients consistent with the number and position of teeth missing due to oral disease. These findings suggest that the quantity of teeth lost does not necessarily reflect the impact of tooth mortality on OHRQoL and that future studies should take this into consideration.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".