The impact of malocclusions on oral health-related quality of life in children—a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: A limited amount of systematic literature reviews on the association between malocclusions and oral health-related quality of life (OHRQOL) summarize inconclusive results. Therefore, we conduct a systematic review and meta-analysis on the association of malocclusions with OHRQOL in children. METHODS: Relevant studies were identified in Pubmed, Embase, Cochrane, Google Scholar and other databases. All studies with data on malocclusions or orthodontic treatment need and OHRQOL in children were included. Methodological quality of the studies was assessed with the Newcastle-Ottawa Scale (NOS). Random effects models were used to estimate summary effect measures for the association between malocclusion and OHRQOL in a continuous and a categorical data analysis. Tests for heterogeneity, publication bias and sensitivity of results were performed. RESULTS: In total, 40 cross-sectional studies were included in the meta-analyses. Summary measures of the continuous data show that OHRQOL was significantly lowered in children with malocclusions (standardized mean difference (95 % CI] = 0.29 (0.19-0.38)). The summary odds ratio for having an impact on OHRQOL was 1.74 times higher in children with malocclusion than in children without malocclusions. Heterogeneity among studies was partly explained by malocclusion assessment, age of the children and country of study conduction. CONCLUSION: Our results provide evidence for a clear inverse association of malocclusion with OHRQOL. We also showed that the strength of the association differed depending on the age of the children and their cultural environment. CLINICAL RELEVANCE: Dentists benefit from understanding the patient differences regarding the impact of malocclusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".