Etiological Factors of Molar Incisor Hypomineralization: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Molar incisor hypomineralization (MIH) is a defect of the dental enamel that predominantly affects first molars and permanent incisors. Identifying the significant risk factors associated with MIH occurrence is essential for the implementation of prevention strategies. The purpose of this systematic review was to determine the etiological factors associated with MIH. A literature search was carried out from six databases until 2022; it covered pre-, peri-, and postnatal etiological factors. The PECOS strategy, PRISMA criteria, and the Newcastle–Ottawa scale were used, and 40 publications were selected for qualitative analysis as well as 25 for meta-analysis. Our results revealed an association between a history of illness during pregnancy (OR 4.03 (95% CI, 1.33–12.16), p = 0.01) and low weight at birth (OR 1.23 (95% CI, 1.10–1.38), p = 0.0005). Furthermore, general illness in childhood (OR 4.06 (95% CI, 2.03–8.11), p = 0.0001), antibiotic use (OR 1.76 (95% CI, 1.31–2.37), p = 0.0002), and high fever during early childhood (OR 1.48 (95% CI, 1.18–1.84), p = 0.0005) were associated with MIH. In conclusion, the etiology of MIH was found to be multifactorial. Children with health disorders in the first years of life and those whose mothers underwent illnesses during pregnancy might be more susceptible to MIH.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".