The use of general anesthesia for dental treatment of children with special healthcare needs in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Children with special healthcare needs (SHCN) often require specialized interventions due to their disabilities. Dental general anesthesia (DGA) is a treatment modality, which improves their access to care but concerns about repeated DGAs persist. AIM: This study investigated DGA utilization in children with SHCN and identified factors associated with multiple DGAs in Alberta, Canada (2010-2020). DESIGN: This retrospective population-based study used administrative data encompassing all children (<18 years) undergoing DGA in publicly funded facilities. Children were identified as SHCN based on their diagnosis codes and categorized into behavioral/psychiatric disorders, mental/intellectual disabilities, physical disabilities, systemic conditions, syndromes/congenital anomalies, physical-mental disabilities, and disabilities with medical conditions. RESULTS: This study analyzed 3884 DGA visits for children with SHCN, predominantly males aged 6-11 and from low-income families. Mental/intellectual disabilities were prevalent (31.8%), and autism was the leading disease. Caries was the primary dental diagnosis across all groups, whereas pulp problems were higher in psychiatric/behavioral disorders (23.6%), and periodontal problems were more common in physical-mental disabilities (13.2%). 28.7% had multiple DGAs, with younger age, disabilities with medical conditions, mental/intellectual disabilities, and initial pulp treatments, increasing the likelihood of multiple DGAs. CONCLUSION: This study highlights the importance of individualized prevention and less conservative treatments for younger children to reduce oral health disparities.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".