Tracking oral health in a standardized, evidence‐based, prevention‐focused dental care system
Bibliographic record
Abstract
OBJECTIVES: Learning health-care systems are foundational for measuring and achieving value in oral health care. This article describes the components of a preventive dental care program and the quality of care in a large dental accountable care organization. METHODS: A retrospective study design describes and evaluates the cross-sectional measures of process of care (PoC), appropriateness of care (AoC), and outcomes of care (OoC) extracted from the electronic health record (EHR), between 2014 and 2019. Annual and composite measures are derived from EHR-based clinical decision support for risk determination, diagnostic and treatment terminology, and decayed-missing-filled-teeth (DMFT) measures. RESULTS: Annually, 253,515 ± 27,850 patients were cared for with 618,084 ± 80,559 visits, 209,366 ± 22,300 exams, and 2,072,844 ± 300,363 clinical procedures. PoC metrics included provider adherence (98.3 percent) in completing caries risk assessments and patient receipt (96.9 percent) of a proactive dental care plan. AoC metrics included patients receiving prevention according to the risk-based protocol. The percent of patients at risk for caries receiving fluoride varnish was 95.4 ± 0.4 percent. OoC metrics included untreated decay and new decay. The 6-year average prevalence of untreated decay was 11.3 ± 0.3 percent, and average incidence of new decay was 13.6 ± 0.5 percent, increasing with risk level: low = 7.5 percent, medium = 18.8 percent, high = 29.4 percent, and extreme = 28.1 percent. CONCLUSIONS: The preventive dental care system demonstrates excellent provider adherence to the evidence-based prevention protocol, with measurably better dental outcomes by patient risk compared to national estimates. These achievements are enabled by a value-centric, accountable model of care and incentivized by a compensation model aligned with performance measures.
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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 | 0.067 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".