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Record W4414167822 · doi:10.1111/scd.70093

Anemia During Pregnancy and Molar Incisor Hypomineralization in Children and Teenagers: Systematic Review and Meta‐Analysis

2025· article· en· W4414167822 on OpenAlexaboutno aff
Beatriz Díaz‐Fabregat, Wilmer Ramírez‐Carmona, Francyenne Maira Castro Gonçalves, Marcela Baraúna Magno, Juliano Pelim Pessan, Douglas Roberto Monteiro, Alberto Carlos Botazzo Delbem, Lucianne Cople Maia, Marcelle Danelon

Bibliographic record

VenueSpecial Care in Dentistry · 2025
Typearticle
Languageen
FieldMedicine
TopicBone and Dental Protein Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAnemiaPregnancyMolarIncisorMolar pregnancy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.276
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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