Social inequalities in tooth loss: A multinational comparison
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it