RELATIONSHIP BETWEEN DENTAL EROSION AND ASTHMA MEDICATION IN CHILDREN: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Introduction: Dental erosion is an alteration that affects the integrity of teeth, which has several aetiologies. It is mentioned that asthma medications may be an etiologic factor; however, studies fail to clarify the possible association between these variables in children with asthma. Therefore, the purpose of this study will be to determine the relationship between dental erosion and asthma medication consumption in paediatric patients. Materials and methods: A systematic research was performed in databases such as MEDLINE (PubMed), Scopus, Web of Science, Scielo, Cochrane Library, Embase, LILACS and gray literature (Open Gray). Two researchers independently selected the articles according to the population, exposure, outcome, study design (PEOS) question using the Rayyan program. Newcastle-Ottawa scale was used to assess the risk of bias. Results: Six articles were included from 120 selected articles. The studies by Bairappan, Domenzain and Arafa studies show a higher prevalence of dental erosion in children with asthma medication as opposed to healthy children, whereas the studies by Dugmore and Rock, Alazmah and Rezende report a higher prevalence of erosive lesions in healthy children. Conclusions: Asthma medications are not a determining factor for the occurrence of erosive lesions in the teeth of paediatric patients with this systemic condition.
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 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".