Diet during Pregnancy and Early Life and Molar Incisor Hypomineralization: A Systematic Review
Bibliographic record
Abstract
Introduction: Molar Incisor Hypomineralisation (MIH) is a clinical, highly prevalent worldwide condition described in the early 2000s. Evidence about its aetiology is still missing, and preventive strategies can’t be established. The aim of this study was to systematically review the literature on possible correlations between maternal and infant nutrition during the first thousand days of life and the occurrence of MIH. Methods: Literature searches of free text and MeSH terms were performed using MedLine (PubMed), Scopus and Google Scholar (from 2017 to February 1st, 2023). Results: The search strategy identified 1815 potential articles: 99 from PubMed, 6 from Scopus, and 1710 from Google Scholar. After removing duplicates, papers not meeting the inclusion criteria or being irrelevant, 6 papers were included. They were published between 2019-2022 and were clinical trials or longitudinal studies conducted in Egypt, New Zealand, Turkey, Denmark, Germany and Norway. The sample size was 2005 subjects (mean: 334). All reporting data on MIH was diagnosed according to the European Academy of Paediatric Dentistry criteria. The nutritional exposure variables were: i) malnutrition; ii) vitamin D serum levels in the mother and the child at different stages; iii) celiac disease; iv) the type of infant feeding, such as exclusive breastfeeding and/or type of weaning; v) supplementation with high-dose vitamin D during pregnancy. Conclusion: High-dose vitamin D supplementation during pregnancy may be protective against MIH in children. There is a need for structured epidemiological studies specifically designed to investigate the aetiology of MIH in mother-child cohorts.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".