Caries Prevalence in Adults with Neurodevelopmental Disorders (ASD and ADHD): A Systematic Review
Bibliographic record
Abstract
Aim: To determine the caries prevalence in adult individuals (aged 18 and older) diagnosed with autism spectrum disorder (ASD) or attention deficit hyperactivity disorder (ADHD) through a systematic review. Materials and Methods: A literature search was carried out in the databases: PubMed, PsycINFO, Scopus and Web of Science. The final search was conducted on the 25th of November 2024. The articles went through duplicate control, title/abstract screening and full text screening according to The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Primary studies in English, with full text available, including subjects aged 18 and older diagnosed with ASD or ADHD, and using the DMF-index as measurement of caries prevalence, were included. The quality assessment was performed with the modified Newcastle Ottawa Scale. Results: A total of 821 potentially relevant studies were identified. Two studies met the inclusion criteria. A total of 70 adults with ASD and 69 controls were included. The studies could not establish a significant relationship between mean DMF-index and ASD. No significant relationship between the severity of ASD and DMF was found. None of the included articles evaluated adults with ADHD. The included studies were assessed to be of low evidence quality. Conclusion: There is limited evidence regarding the relationship between cariesprevalence and adults with ASD. Thus, there is a need for additional research on thesubject. Moreover, there is a need for more research on whether there is an association between caries prevalence and adults with ADHD.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.012 | 0.008 |
| 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) | 0.003 | 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".