Association between respiratory diseases and molar-incisor hypomineralization: A systematic review and meta-analysis
Bibliographic record
Abstract
The molar-incisor permineralização (MIH) is a qualitative enamel developing abnormality involving the occlusal and/or incisal third of one or more molars or permanent incisors, caused by systemic factors. Several systemic disorders and environmental factors, such as respiratory diseases, have been reported as probable causes of MIH. Thus, this work aimed to evaluate whether respiratory diseases and MIH are associated. The searches were carried out in electronic databases, including PubMed, Scopus, Web of Science, the Cochrane Library, LILACS, OpenGrey, and Google Scholar. The acronym PECO was used, in which the P (population) was humans in permanent dentition stage; (E-exposure) molar-incisor hypomineralization; (C-comparison) reference population and (O - outcome) respiratory diseases. After the search retrieval, the duplicates were removed, and the articles were evaluated by title and abstract; then, the papers were read and thoroughly assessed. After selection, the risk of bias assessment was performed using the Newcastle-Ottawa Scale (NOS) for observational studies. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) tool was used to assess the level of evidence. Three thousand six hundred and sixty six were found through the searches. After exclusion by duplicates, title, abstract, and full-reading, 13 articles remained. The articles included in this review evaluated the association of MIH with asthma, tonsilitis, pneumonia, and bronchitis. Most reports showed a low risk of bias. When exploring through GRADE, a very low level of evidence was found. We observed that the included studies showed that children with MIH had more respiratory diseases than the group that did not have MIH. Systematic review registration: https://osf.io/un76d.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".