A Global Perspective of Racial–Ethnic Inequities in Dental Caries: Protocol of Systematic Review
Bibliographic record
Abstract
Though current evidence suggests that racial-ethnic inequities in dental caries persist over time and across space, their magnitude is currently unknown from a global perspective. This systematic review aims to quantify the magnitude of racial/ethnic inequities in dental caries and to deconstruct the different taxonomies/concepts/methods used for racial/ethnic categorization across different populations/nations. This review has been registered in PROSPERO; CRD42021282771. An electronic search of all relevant databases will be conducted until December 2021 for both published and unpublished literature. Studies will be eligible if they include data on the prevalence or severity of dental caries assessed by the decayed, missing, filled teeth index (DMFT), according to indicators of race-ethnicity. A narrative synthesis of included studies and a random-effects meta-analysis will be conducted. Forest plots will be constructed to assess the difference in effect size for the occurrence of dental caries. Study quality will be determined via the Newcastle-Ottawa Scale and the GRADE approach will be used for assessing the quality of evidence. This systematic review will enhance knowledge of the magnitude of racial/ethnic inequities in dental caries globally by providing important benchmark data on which to base interventions to mitigate the problem and to visualize the effects of racism on oral health.
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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 | 0.099 | 0.111 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.016 | 0.019 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.073 | 0.009 |
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".