The Oral Health Status and Treatment Needs of Pediatric Patients Living with Autism Spectrum Disorder: A Retrospective Study
Bibliographic record
Abstract
Background: The objective of this retrospective study was to assess the oral health status and treatment needs of children with ASD and to explore the differences in risk factors and oral health care status and the risk factors for treatment under GA. Methods: Dental charts of children between 6 and 14 years of age who were examined at a dental facility associated with the College of Dentistry, University of Saskatchewan between 2016 to 2019 were assessed. Children who were identified as having ASD, as well as an age- and gender-matched control group consisting of otherwise healthy children were included in the study. Results: The sample included 346 dental records, with 173 children having a diagnosis of ASD. Children diagnosed with ASD had significantly higher experience with caries (91.3% vs. 65.9%, p = 0.003) and severity (mean DMFT/dmft = 8.18 ± 1.62 vs. 4.93 ± 0.58 p = 0.007). Children with ASD were also older when visiting the dentist for the first time (age of 5.97 ± 1.18 vs. 2.79 ± 1.09, p = 0.02)). Children with ASD were less likely to brush once a day (66.5% vs. 88.4%, p = 0.02), were more likely to have bruxism (35.8% vs. 10.4%, p = 0.003) and were less likely to have class I occlusion (64.7% vs. 80.9%, p = 0.03). Findings from the logistic regression analysis revealed that children with ASD were also 2.13 times more likely to receive a referral for general anesthesia when all other variables were held constant (p = 0.03). Conclusions: This research demonstrates that children diagnosed with ASD may face more barriers with access to oral health care, leading to poorer outcomes and greater treatment dental needs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".