The Changes in Orthodontic Treatment Need in Children Over Time: A Longitudinal Evaluation of Self-Correcting Malocclusions
Bibliographic record
Abstract
Objective: This systematic review aimed to synthesize longitudinal evidence on the natural changes in orthodontic treatment need among children and adolescents, with a specific focus on identifying malocclusion traits that demonstrate a potential for spontaneous correction over time. Methods: A systematic search was conducted across PubMed/MEDLINE, SienceDirect, Cochrane Library and Google Scholar from inception to November 2025, following PRISMA guidelines. Eligible studies were longitudinal cohorts assessing children and adolescents in mixed or early permanent dentition, with no prior orthodontic treatment at baseline. Outcomes included changes in treatment need measured by indices like the Dental Aesthetic Index (DAI) or Index of Orthodontic Treatment Need (IOTN), and observed self-correction of specific malocclusion traits. Risk of bias was assessed using the Newcastle-Ottawa Scale. Results: Four prospective cohort studies (n=1,253 participants) were included. The evidence revealed a non-linear trajectory of malocclusion prevalence, characterized by an initial decrease from primary to mixed dentition, followed by an increase in early permanent dentition. Despite this, a net decrease in treatment need was observed for many individuals during the transition from mixed to permanent dentition, with one study reporting decreased DAI scores for 60.8% of children. High rates of spontaneous correction were documented for specific traits: anterior open bite (87-99%), Class II malocclusion (83%), and unilateral posterior crossbite (83%). Nevertheless, a persistent, clinically significant treatment need remained, with 22% of 11.5-year-olds classified as having severe or extreme need. Conclusion: Orthodontic treatment need in children is dynamic, not static. While significant self-correction occurs for traits like anterior open bite and posterior crossbite, a substantial proportion of children develop a definitive need for intervention by early permanent dentition. These findings underscore the importance of longitudinal monitoring and cautious timing of orthodontic assessments, particularly during mixed dentition when transient traits may overestimate true long-term need.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".