Prevalence of Molar-incisor hypomineralization in Iranian children – A systematic review and narrative synthesis
Bibliographic record
Abstract
INTRODUCTION: Molar-incisor hypomineralization is a developmental defect of enamel with clinical features vary from demarcated opacities to severe tissue breakdown which calls for considerable preventive and interceptive measures. The aim of this article was to systematically review the literature on the prevalence of MIH in Iran and highlight the condition in Iranian children. MATERIALS AND METHODS: A systematic search of literature was conducted in Scopus, Pubmed, Ovid, Embase, Web of Science, and Google-Scholar as well as national Iranian database and digital archives of dental schools from the beginning of 2000 to the end of 2021 for published and unpublished studies. Data from cross-sectional, cohort, and case-control studies on prevalence of molar-incisor hypomineralization among 6-13-year-old children was gathered, using the following MeSH terms and keywords and their Persian equivalents: Prevalence, Hypomineralisation, Hypomineralization, MIH, "molar incisor", "molar-incisor", "cheese molars", "Hypomineralised first permanent molars", "Hypomineralized first permanent molars", "developmental defects of enamel", "enamel developmental defects", Iran*. Methodological quality and the risk of bias of quantitative studies was assessed using a modified version of Newcastle-Ottawa Scale. Due to the considerable clinical and statistical heterogeneity of the included studies, pooling of data through meta-analysis was not possible. Therefore, a descriptive synthesis of data was performed. RESULTS: Fifteen cross-sectional studies with a total number of 12011 participants were included in the systematic review. The prevalence of MIH ranged from 5.1% to 25.6%. All of the included studies were at a moderate risk of bias (NOS of 4-6). The lowest prevalence of MIH was reported in Kerman (5.14%) and the highest in Tehran (25.6%). Substantial methodological, clinical and statistical heterogeneity was observed. CONCLUSION: This is the first study to systematically review the available literature on MIH prevalence in Iran. However, the present review has some limitations such as limited number of included studies, large heterogeneity of the research, and moderate quality of included studies. Further high-quality research is warranted.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".