Prevalence of Malocclusion Traits in Primary Dentition, 2010–2024: A Systematic Review
Bibliographic record
Abstract
The present review was aimed to describe the prevalence and the regional distribution of malocclusion among preschool children worldwide. Two independent reviewers performed a systematic literature search to identify English publications from January 2010 to May 2024 using PubMed, ISI Web of Science and Embase. Search MeSH key words were “malocclusion”, “primary dentition” and “child, preschool”. The reporting quality was assessed by the modified Newcastle–Ottawa Quality Assessment Scale. We identified 2599 publications and recruited 47 articles. Fourteen of the included studies were conducted in Asia, four in Europe, twenty-eight in South America and one in Africa. The prevalence of malocclusion ranged from 28.4% to 83.9%, and half of the reported prevalences were higher than 50%. The highest percentage was in Asia (61.81%), followed by Europe (61.50%), South America (52.69%) and Africa (32.50%). Statistically significant differences existed in deep overbite, anterior open bite, posterior crossbite, edge-to-edge incisor relationship and distal step between continents (p < 0.05). Europe showed the highest prevalence (33.08%) of deep overbite. Africa showed the highest prevalence (18.60%) of anterior open bite. Europe showed the highest prevalence (15.38%) of posterior crossbite. The most common malocclusion traits were increased overjet and deep overbite. To conclude, malocclusion remained prevalent in the primary dentition and varied between countries.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".