Increasing value, reducing waste: tailoring the application of dental sealants according to individual caries risk
Bibliographic record
Abstract
OBJECTIVES: Despite a significant national investment in oral health, there is little understanding of the return in terms of quality. Value-based payments aim to refocus provider reimbursement based on the value created to the patient. Our objectives were to apply a set of dental quality measures to help determine the value of preventive dental care provided to children at two academic dental school clinics. METHODS: We queried the institutional electronic health records (EHRs) for patients between the ages of 6-14 years with sealable first or second permanent molars, determined caries risk status, identified if dental sealants were placed, and finally if the teeth showed evidence of new caries experience. In order to determine the cost-effectiveness of EHR-based triage of applying dental sealants, we calculated the incremental cost-effectiveness ratio (ICER) for the dental quality measures supported sealing program. RESULTS: Between the two academic sites, there were 6,155 unique children for a total of 12,302 eligible teeth without a sealant and 32,811 eligible teeth with a sealant. Teeth without a sealant were more likely to have decay (4.8 percent) than those with a sealant (1.7 percent). At both sites, patients with high caries risk were more likely to benefit from sealants compared to those patients with low risk. CONCLUSION: Implementation of caries risk stratified fissure sealant quality measures demonstrates the potential for extracting better value in oral health care.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".