Is Malocclusion Associated with Dental Caries among Children and Adolescents in the Permanent dentition? A Systematic Review.
Bibliographic record
Abstract
OBJECTIVE: To determine the association between malocclusion and the severity of dental caries among children and adolescents in the permanent dentition. METHOD: A search was conducted in Medline, Cochrane databases, Google scholar, Scopus and Web of Science through October 2020 for studies of malocclusion and dental caries among children and adolescents using the Dental Aesthetic Index (DAI) and the Decayed, Missing, Filled Teeth (DMFT) index. Quality was evaluated using the Newcastle-Ottawa tool for cross-sectional studies. Data were extracted using the Cochrane Collaboration guidelines. Meta-analysis used the Cochrane Program Review Manager Version 5. A random effects model was used to assess the association among different categories of malocclusion with dental caries. GRADE analysis assessed the certainty of evidence. RESULTS: Five studies met the inclusion criteria. Handicapping malocclusion was significantly associated with higher mean DMFT scores (Mean difference: 1.03, 95% CI, 0.61, 1.44). Participants with severe malocclusion had higher mean DMFT when compared to subjects with normal occlusion (0.32, 95% CI, 0.13, 0.51). Definite malocclusion was also associated with higher mean DMFT scores (Mean difference: 0.19, 95% CI, 0.03, -0.35). CONCLUSION: Malocclusion is associated with dental caries in the permanent dentition. DMFT scores and the strength of the association increased with severity of malocclusion. Low to moderate certainty of evidence was observed for association between handicapping, severe, and definite malocclusion 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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".