Molar Incisor Hypomineralization and Periodontitis in Adolescents: A Population‐Based Study
Bibliographic record
Abstract
AIM: To investigate the association between molar incisor hypomineralization (MIH) and periodontitis in adolescents. METHODS: A population-based study was undertaken of Brazilian adolescents aged 18 and 19 years (n = 2515). MIH was assessed according to Ghanim's criteria. The outcomes were Periodontitis Indicators, a continuous latent variable estimated on the basis of the common variance shared by the indicators bleeding on probing (BoP), probing pocket depth (PPD ≥ 4 mm) and clinical attachment level (CAL ≥ 3 mm), as well as periodontitis cases defined according to CDC-AAP. Structural equation modelling was used, and the models were adjusted for lower socioeconomic status, sex, smoking, triglycerides/high-density lipoprotein (TG/HDL) (mg/dL) and visible plaque index. Two other approaches were used for sensitivity analysis: (i) logistic regression, considering the effect of MIH on periodontitis (CDC/AAP) at the individual level, and (ii) multilevel regression to evaluate the association of MIH with CAL and PPD, considering the tooth as the unit of analysis, adjusted for other tooth-level and individual-level variables. RESULTS: The prevalence of MIH was 16.86% (n = 423). Individuals with MIH had higher values of the Periodontitis Indicators through a direct pathway (standardized coefficient [SC] = 0.193, p < 0.001) and indirectly mediated by biofilm (SC = 0.263, p < 0.001). MIH was also associated with periodontitis according to CDC-AAP (SC = 0.071, p < 0.001) and indirectly mediated by biofilm (SC = 0.344, p < 0.001). Consistently, lower socioeconomic status, sex, TG/HDL and biofilm were associated with both periodontal outcomes. Tooth-level sensitivity regression analysis confirmed the association observed in individual-level analyses. CONCLUSION: Our findings suggest that adolescents with MIH are susceptible to periodontitis, and it is therefore important to monitor their periodontal health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".