Dental Anxiety in Children With Autism Spectrum Disorder: Understanding Frequency and Associated Variables
Bibliographic record
Abstract
Dental anxiety seems to be elevated in children with autism spectrum disorder (ASD), and may be associated with feelings of helplessness, loss of control, and sensory overload. Dental anxiety, a primary contributor to dental avoidance, can lead to unwanted long-term oral hygiene consequences. This manuscript characterizes the frequency and correlates of dental anxiety in children with ASD. Specifically, this study examined associations between child-reported dental anxiety and parent-reported autism symptom severity, anxiety symptom severity, sensory sensitivity, and internalizing/externalizing symptom severity. Participants included 76 children without cognitive impairment (age in years M = 9.9, SD = 1.8) who took part in a cognitive behavioral therapy study for children with ASD and co-occurring anxiety disorders. Elevated dental anxiety was found in 68% of participants based on a cut-off score from a dental anxiety measure, with fears related to pain being the most commonly endorsed concern; over half of youth endorsed feeling scared about pinching feelings or having a tooth pulled out at the dentist. No significant correlations between dental anxiety and other variables of interest were found, including overall anxiety severity, ASD symptoms, internalizing and externalizing symptoms, and sensory sensitivities. The findings contextualize the frequency of dental anxiety and its relationship to various variables, which may be useful in tailoring existing treatments to reduce dental anxiety in children with ASD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".