Anemia During Pregnancy and Molar Incisor Hypomineralization in Children and Teenagers: Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
INTRODUCTION: Molar incisor hypomineralization (MIH) is an enamel defect that has a systemic origin and affects at least one permanent first molar and incisors. AIM: The present systematic review aims to answer the question: "Is there an association between maternal anemia during pregnancy and the occurrence of MIH in children and adolescents?" METHODS: Studies in children and teenagers affected with MIH compared to healthy children and teenagers, assessing anemia in the gestational period, with no data or language restrictions, were eligible. The search in eight databases was performed up to January 2024. Quality assessment was assessed using by Newcastle Ottawa Scale tool. The association between anemia during the gestational period and the MIH was assessed by odds ratio (OR) and its corresponding 95% confidence interval (CI) in the RevMan software. The certainty in the body of evidence was assessed for pooling of outcomes using the GRADE approach. RESULTS: One case-control and three cross-sectional studies were included. A positive association between anemia during the gestational period and MIH was measured (OR 1.84 [1.28; 2.65]) (biases were detected). CONCLUSIONS: The results show with a very low certainty of evidence that anemia is associated with greater involvement of MIH in children and teenagers.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".