Using electronic dental records to establish a surveillance system for dental decay in rural Western Alaska
Bibliographic record
Abstract
OBJECTIVES: Previous surveys have demonstrated high rates of early childhood caries (ECC) in the Alaska Native (AN) population of western Alaska. There are many challenges to providing dental care in this road-less Yukon-Kuskokwim Delta region. The regional Tribal Health Organization implemented an electronic dental record (EDR) system in the late 1990s. We explored use of the EDR to establish an oral health surveillance system in children. METHODS: We contracted with EDR software developers to implement calculation of a summary count of decayed (d), missing (m) or filled (f) primary (dmft) score for each individual. We calculated the yearly average dmft scores for 2011-2019 for children aged 3 and 5 years with a comprehensive exam in a given year. We also assessed the number of children undergoing full mouth dental rehabilitation (FMDR). We used US census data population estimates for these age groups to calculate rates. RESULTS: Over the 9-year period, 2,427 3-year-old children (47 percent of all 3-year olds over this period), received a comprehensive exam; increasing from 24 percent in 2011 to 62 percent in 2019. Their average dmft score over the 9-years was 6.4 with a significant annual decline over this period. Seventy percent of AN children who turned 6 between 2015 and 2019 had received at least one FMDR. CONCLUSIONS: An oral health surveillance system has been established in western Alaska using the Electronic Dental Record. High rates of ECC and FMDR were observed. This surveillance system will allow assessments of ECC prevalence and impact of dental interventions.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".