System level interventions to reduce utilization of general anesthesia to treat dental caries: a practice brief
Bibliographic record
Abstract
Dental caries are the most common chronic disease of childhood. Untreated caries can result in severe pain and infection; and in some cases, difficulties in eating, speech, and education. Hospitalization and general anesthesia are often necessary for treating extensive disease in young children, which adds significant risk and expense. Interventions, such as community-based preventative care, utilization of pre-authorizations for treatment, and at-risk contracts, have been deployed as innovative strategies to reduce the incidence of caries and the cost of treatment. Value-based payment structures give payors flexibility to design a multipronged system to impact the health of consumers. This practice brief will identify interventions at a systems level that reduced the utilization of general anesthesia treating dental caries in children under the age of six. Dental claims data from the period of Q1 2011 to Q2 2020 were utilized to analyze the trends in utilization of the operating room (OR) to treat dental conditions among children under 6 years. Fixed effects analysis was utilized to identify key over time changes in the reduction of children's OR utilization. A reduction in utilization of general anesthesia and hospitalization for treating dental caries in young children was seen. The expansion of the community care team, metrics to reinforce the systems of provider education and training, and the use of incentive payments were all associated with reductions in the rate of OR utilization. Between 2012 and 2017, multiple initiatives were implemented without a systematic approach to quality improvement to evaluate.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".