Practice Patterns Among Dentist Anesthesiologists for Pediatric Patients with Autism Spectrum Disorders.
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate practice patterns among dentist anesthesiologists for pediatric patients with autism spectrum disorders (ASD) undergoing sedation for dental procedures. METHODS: An electronic nationwide survey was delivered to all members of the American Society of Dentist Anesthesiologists. The survey assessed provider training and comfort in treating pediatric patients with ASD, perioperative procedures for children with and without ASD, and preferred educational resources for the perioperative management of pediatric patients with ASD. RESULTS: Respondents were 114 dentist anesthesiologists and residents (33.3 percent response rate). Respondents indicated a high comfort level for managing pediatric patients with ASD for sedation (mean equals 91.9±14.74 [SD] percent). The average number of patients with ASD who respondents treat per week was 3.48±2.44). Providers reported making scheduling and staffing accommodations for patients with ASD. More than half of respondents reported no difference between patient groups in medication dosing for sedation and medication regimens used intraoperatively; however, only 43.9 percent of providers indicated using equivalent preoperative medication regimens for both patient groups, and providers reported increased usage of preoperative anxiolytic techniques with patients with ASD. Importantly, 87.7 percent of respondents reported the same incidence of adverse events during the perioperative period between groups. CONCLUSIONS: Findings from this survey suggest there are both similarities and differences in how dentist anesthesiologists practice with pediatric patients with and without autism spectrum disorders. Additional research is warranted to measure the clinical benefits of modified practices for patients with ASD and identify best practices for this vulnerable population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".