Global Oral Health Inequalities
Bibliographic record
Abstract
The birth prevalence of orofacial clefts, one of the most common congenital anomalies, is approximately one in 700 live births, but varies with geography, ethnicity, and socio-economic status. There is a variation in infant mortality and access to care both between and within countries, so some clefts remain unrepaired into adulthood. Quality of care also varies, and even among repaired clefts there is residual deformity and morbidity that significantly affects some children. The two major issues in attempts to address these inequalities are (a) etiology/possibilities for prevention and (b) management and quality of care. For prevention, collaborative research efforts are required in developing countries, in line with the WHO approach to implement the recommendations of the 2008 Millennium Development Goals (www.un.org/millenniumgoals). This includes the "common risk factor" approach, which analyzes biological and social determinants of health alongside other chronic health problems such as diabetes and obesity, as outlined in the Marmot Health inequalities review (2008) (www.ucl.ac.uk/gheg/marmotreview). Simultaneously, orofacial cleft research should involve clinical researchers to identify inequalities in access to treatment and identify the best interventions for minimizing mortality and residual deformity. The future research agenda also requires engagement with implementation science to get research findings into practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.001 |
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".