Visualizing the Comorbidity Burden in Children with Autism Spectrum Disorder Receiving Dental Treatment Under General Anesthesia.
Bibliographic record
Abstract
PURPOSE: The purposes of this study were to: (1) describe the comorbidity burden in children with autism spectrum disorder (ASD) receiving dental treatment under general anesthesia (GA); and (2) characterize the complexity of these concurrent comorbidities. METHODS: A retrospective chart review was completed of 303 children with ASD who received dental treatment under GA. All comorbidities, in addition to the primary diagnosis of ASD, were categorized using the International Classification of Diseases-10 codes. The interconnectedness of the comorbidities was graphically displayed using a network plot. Network indices (degree centrality, betweenness centrality, closeness centrality) were used to characterize the comorbidities that exhibited the highest connectedness to ASD. RESULTS: The network plot of medical diagnoses for children with ASD was highly complex, with multiple connected comorbidities. Developmental delay, speech delay, intellectual disability, and seizure disorders exhibited the highest connectedness to ASD. CONCLUSIONS: Children with autism spectrum disorder may have a significant comorbidity burden of closely related neurodevelopmental disorders. The medical history review should assess the severity of these concurrent disorders to evaluate a patient's potential ability to cooperate for dental treatment and to determine appropriate behavior guidance techniques to facilitate the delivery of 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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".