Comparative Survival of Restorations in MIH-Affected Pediatric Teeth Using Total-Etch Versus Self-Etch Adhesive Systems: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
This study aimed to investigate adhesive techniques applied to MIH-affected teeth by analyzing the one-year failure rate of restorations performed using total-etch and self-etch adhesive methods. This systematic review was conducted in accordance with the PRISMA 2020 statement. Eligibility criteria were defined using the PICO acronym. In vivo studies published from 2017 onward were evaluated. Two independent reviewers conducted the search on PubMed, Scopus, Cochrane, and Web of Science. The risk of bias was assessed with RoB2 and the Newcastle-Ottawa Scale. Statistical analysis was performed using the Open Meta [Analyst] software based on the absolute risk of failure. Results were presented as a pooled estimate with a 95% confidence interval (CI) and visualized in forest plots. Four RCTs and one retrospective cohort study were selected for the analysis. Data collected included information such as authors, study design, age, restorations, degree of hypomineralization, protocol, and follow-up. The meta-analysis showed no statistically significant differences between the techniques (p = 0.338) on MIH-affected teeth. This meta-analysis supports the use of both adhesive techniques for managing MIH teeth, emphasizing the need for further studies to examine the specific clinical and technical conditions under which each technique might be more advantageous.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".