Effect of Prevalence and Severity of Molar-Incisor Hypomineralization on Oral Health-Related Quality of Life: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objectives: The aim of this systematic review and meta-analysis is to assess the association between the MIH presence as well as the severity and OHRQoL in children. Material and methods: Relevant studies were identified in PubMed, Embase, Cochrane and Google Scholar. Studies involving MIH and OHRQoL in children were included. A methodological quality assessment of included studies was performed using the Newcastle-Ottawa Scale (NOS) and its adapted version for cross-sectional studies. Random effects models were used to estimate summary effect measures for the association between MIH presence (presence vs. absence) as well as severity (moderate/severe MIH vs. no MIH) and OHRQoL using generic inverse variance meta-analyses. Tests for heterogeneity, publication bias and sensitivity of results were also performed. Results: Out of 1696 identified publications 11 studies reporting on 5,017 children were included in the meta-analysis assessing the impact of MIH presence. There was no statistically significant association between the presence of MIH and lower OHRQoL in affected children (OR = 1.72, 95% CI = 0.99-2.98). Concerning MIH severity and its impact on OHRQoL, a sum of 6 studies were included in the meta-analysis involving a total of 2,595 children. There was a significant association between moderate/severe MIH and lower OHRQoL in affected children (OR = 3.43, 95% CI = 1.69-6.98). Conclusion: Moderate/Severe MIH has a significant and clinically relevant negative impact on OHRQoL, and it should therefore be addressed adequately. Future research should also consider the impact of a uniform MIH diagnosis and precise severity criteria.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.059 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".