Prevalence of molar incisor hypomineralization in India: A systematic review and meta-analysis
Bibliographic record
Abstract
Research Question: To estimate the pooled prevalence of molar incisor hypomineralization (MIH) in children from India. Research Protocol: The PRISMA guidelines were followed. Literature Search: An electronic search of the databases was performed to find prevalence studies of MIH in children above age 6 years in India. Data Extraction: Two authors independently extracted the data from the 16 included studies. Quality Appraisal: The risk of bias was assessed using a modified version of the Newcastle-Ottawa Scale adapted for cross-sectional studies. Data Analysis: statistic. The subgroups were analyzed to assess the pooled prevalence of MIH according to sex, arch-wise proportion of MIH-affected teeth, and proportion of children with the MIH phenotypes. Results and Interpretation of Results: Sixteen studies included in the meta-analysis represented 7 states of India. A total of 25,273 children were included in the meta-analysis. The pooled prevalence of MIH in India was estimated to be 10.0% (95% CI: 0.07, 0.12) with significantly high heterogeneity between the included studies. The pooled prevalence did not vary according to sex. The pooled proportions of MIH-affected teeth were similar in the maxillary and mandibular arches. The pooled proportion of children with MH phenotype was higher (56%) than those with M + IH phenotype (44%). Further studies with standardized criteria for recording MIH are needed to ascertain the prevalence of MIH in India.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".