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RELATIONSHIP BETWEEN DENTAL EROSION AND ASTHMA MEDICATION IN CHILDREN: A SYSTEMATIC REVIEW

2025· review· en· W4408214876 on OpenAlexaboutno aff
André Alexis Díaz-Quevedo, Diana Guadalupe Anaya Rubina, Cárol Magaly Cárdenas Flores

Bibliographic record

VenueRevista Científica Odontológica · 2025
Typereview
Languageen
FieldDentistry
TopicDental Erosion and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAsthmaCochrane LibraryScopusMEDLINEPopulationDentistryPediatricsMeta-analysisEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.067
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.032
GPT teacher head0.332
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueRevista Científica OdontológicaSame topicDental Erosion and TreatmentFrench-language works237,207