Relationship between preterm birth and developmental defects of enamel: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: A putative relationship between preterm birth and developmental defects of enamel (DDE) has been described in the literature. Although systematic reviews have found preterm birth may lead to DDE, the effect size has not been quantified. AIM: The aim of this systematic review and meta-analysis was to determine the association between preterm birth and DDE. DESIGN: An electronic search was performed in PubMed, Cochrane Library, Scopus, and Web of Science to identify relevant studies. Two independent reviewers selected the studies in a two-stage process in accordance with the PRISMA statement. The risk of bias was also analysed using the Newcastle-Ottawa Scale criteria. RESULTS: A total of 1041 publications were considered after an electronic search, 20 of which were included in the systematic review. Of these 20 publications, 18 articles were included in a meta-analysis. The meta-analysis detected an increased risk of developing DDE in preterm children [OR: 3.27 (95% CI 2.02, 5.30; P < .001)], with a greater risk in the primary dentition. In addition to this, a subgroup analysis showed a greater risk in the development of hypoplasia in preterm children. CONCLUSIONS: The results of this meta-analysis showed a three times increased risk of developing DDE in preterm children.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| 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 | 0.002 |
| 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".