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Record W4411322972 · doi:10.5409/wjcp.v14.i3.106778

Challenges and solutions in managing dental problems in children with autism

2025· article· en· W4411322972 on OpenAlexaboutno aff
Mohammed Al‐Beltagi, Abdulrahman Abdullah Al Zahrani, Babu Sandilyan Mani, Ehab M. Hantash, Nermin Kamal Saeed, Adel Salah Bediwy, Reem Elbeltagi

Bibliographic record

VenueWorld Journal of Clinical Pediatrics · 2025
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionMedicineAutismInclusion (mineral)Autism spectrum disorderMEDLINEIntervention (counseling)Thematic analysisCochrane LibraryFamily medicineQualitative researchNursingPsychologyRandomized controlled trialPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Children with autism spectrum disorder (ASD) face unique challenges in maintaining oral health due to sensory sensitivities, communication difficulties, and behavioral barriers. These factors, along with limited access to ASD-trained dental professionals, increase their risk of dental caries, periodontal disease, bruxism, and other oral health issues. Despite growing awareness of these challenges, a comprehensive synthesis of evidence-based solutions remains lacking. AIM: To review synthesizes existing research on dental problems in ASD, barriers to care, management strategies, and future directions for improved oral health outcomes. METHODS: A systematic search of PubMed, Cochrane Library, and Scopus was conducted using predefined search terms. Related to ASD, dental health, and management strategies. Inclusion criteria encompassed studies focusing on children with ASD, dental health issues, and interventions. Data extraction included study design, participant characteristics, key findings, and intervention outcomes. The quality of studies was assessed using appropriate tools such as the Cochrane Risk of Bias Tool and the Newcastle-Ottawa Scale. A narrative synthesis approach, incorporating thematic analysis, was utilized to evaluate the findings. RESULTS: A total of 165 studies met the inclusion criteria. Children with ASD exhibited a higher prevalence of dental caries, gingivitis, bruxism, and malocclusion compared to neurotypical peers. Barriers to dental care included sensory sensitivities, communication difficulties, financial constraints, and a shortage of ASD-trained dental professionals. Effective interventions included desensitization programs, behavioral therapy, digital applications, and interdisciplinary collaboration. Parental education and professional training were crucial for improving oral health outcomes. CONCLUSION: Tailored dental care strategies, including sensory adaptations, behavioral interventions, and interdisciplinary collaboration, are essential for children with ASD. Standardized guidelines and long-term studies are needed to refine evidence-based protocols. Future research should explore digital interventions and probiotic applications in ASD dental care.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.368
Teacher spread0.320 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations6
Published2025
Admission routes1
Has abstractyes

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