Social inequalities in tooth loss: A multinational comparison
Bibliographic record
Abstract
OBJECTIVES: To conduct cross-national comparison of education-based inequalities in tooth loss across Australia, Canada, Chile, New Zealand and the United States. METHODS: We used nationally representative data from Australia's National Survey of Adult Oral Health; Canadian Health Measures Survey; Chile's First National Health Survey Ministry of Health; US National Health and Nutrition Examination Survey; and the New Zealand Oral Health Survey. We examined the prevalence of edentulism, the proportion of individuals having <21 teeth and the mean number of teeth present. We used education as a measure of socioeconomic position and measured absolute and relative inequalities. We used random-effects meta-analysis to summarize inequality estimates. RESULTS: The USA showed the widest absolute and relative inequality in edentulism prevalence, whereas Chile demonstrated the largest absolute and relative social inequality gradient for the mean number of teeth present. Australia had the narrowest absolute and relative inequality gap for proportion of individuals having <21 teeth. Pooled estimates showed substantial heterogeneity for both absolute and relative inequality measures. CONCLUSIONS: There is a considerable variation in the magnitude of inequalities in tooth loss across the countries included in this analysis.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".