Association of Medication Use with the Development of Enamel Defects in Pregnancy and Childhood: A Critical Review of the Literature
Bibliographic record
Abstract
Aims: This review critically evaluated the scientific evidence regarding the association between medication exposure during pregnancy or early childhood and the development of Developmental Enamel Defects (DDEs) in children. Study Design: Critical literature review. Methodology: A systematic search was conducted across six databases and the gray literature, following PRISMA guidelines and registered in PROSPERO (CRD420251047079). Observational cohort studies published from 2001 onward were included. Study quality was assessed using the Newcastle-Ottawa Scale (NOS). The search was conducted on May 20, 2025, in the PubMed, Cochrane Library, Web of Science, Scopus, BVS, and Embase databases. A gray literature search was also conducted, analyzing the first 100 references in Google Scholar. Results: Of 1.362 records initially retrieved, 10 cohort studies met the inclusion criteria. No randomized clinical trials were found. The studies investigated various medications, including antibiotics, corticosteroids, asthma drugs, antivirals, bisphosphonates, and vitamin D supplements. While some studies reported associations—particularly with amoxicillin and molar-incisor hypomineralization (MIH)—others found no statistically significant links. NOS scores ranged from 6 to 9 stars, indicating moderate to high methodological quality. Although causal relationships could not be established, observational studies remain a viable approach to investigating drug associations in tooth development. Conclusion: This review found a possible association between medication use and enamel developmental defects, but no causal relationship was established. Further well-designed studies are needed to strengthen the evidence and guide pediatric dentistry practice and policy.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".