A Systematic Review of Socioeconomic Indicators and Dental Caries in Adults
Bibliographic record
Abstract
Increasing evidence suggests that socioeconomic factors may be associated with an increased risk of dental caries. To provide better evidence of the association between dental caries in adults and socioeconomic indicators, we evaluated the relation between these two conditions in a thorough review of the literature. Seven databases were systematically searched: Pubmed, Cochrane, Web of Science, Bireme, Controlled Trials, Clinical Trials and the National Institute for Health and Clinical Excellence. No restrictions were placed on the language or year of publication. The search yielded 41 studies for systematic review. Two independent reviewers screened the studies for inclusion, extracted data and evaluated quality using the Newcastle-Ottawa scale. The following socioeconomic indicators were found: educational level, income, occupation, socio-economic status and the community index. These indicators were significantly associated with a greater occurrence of dental caries: the subject's education, subject's income, subject's occupation and the Gini coefficient. A high degree of heterogeneity was found among the methods. Quality varied across studies. The criteria employed for socioeconomic indicators and dental caries should be standardized in future studies. The scientific evidence reveals that educational level, income, occupation and the Gini coefficient are associated with dental caries.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".