Are Molar-Incisor Hypomineralization and Hypomineralized Second Primary Molars Predictive of Dental Caries?: A Systematic Review
Bibliographic record
Abstract
Abstract To estimate the prevalence of dental caries among children affected concomitantly with molar-incisor hypomineralization (MIH) and hypomineralized second primary molars (HSPM). Electronic databases, including Medline via PubMed, Cochrane Library, Scopus, and Science Direct, were searched. Studies written in English involving children with MIH-HSPM and dental caries were considered eligible. Two reviewers extracted the data according to the PRISMA statement and assessed the bias risk with the Newcastle–Ottawa Scale (NOS) criteria. Out of 535 studies identified in the initial research, only two articles were included in the present review. A significant association between MIH-HSPM and dental caries in permanent dentition was reported in the two included records (AOR: 3.70; p < 0.001). Children with MIH-HSPM expressed significantly higher DMFT and PUFA values than children without MIH-HSPM. However, dmft values did not differ significantly between children with and without HSPM (1.79 [2.4] vs. 1.78 [2.5]; 1.88 [2.73] vs. 1.59 [2.53], respectively. MIH-HSPM lesions were correlated with a higher prevalence and more severe carious lesions in permanent dentition. However, the results should be interpreted cautiously because the findings were generated by only two studies performed by the same authors. Thus, further well-designed studies are highly recommended.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.011 | 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) | 0.005 | 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".