Associations between utilization of dental care and oral health outcomes in the U.S. using the National Health and Nutrition Examination Survey (2017-2020)
Bibliographic record
Abstract
Abstract Background This analysis aims to evaluate the association between the time since and reason for a patient's last dental appointment across clinical oral health outcomes. Methods We used data from the 2017–2020 National Health and Nutrition Examination Survey (NHANES), a cross-sectional nationally-representative of US noninstitutionalized adults. The predictors were the time since last dental appointment and the reason for the last dental appointment (routine vs. urgent). We examined the presence and number of missing teeth and teeth with untreated coronal and root caries. Multivariable regression models were used to assess the interaction between time since last dental appointment and reason of the appointment on clinical oral health outcomes. Results Two-thirds of the US population had a dental appointment within a year, while 53 million individuals did not visit a dentist for the last three years. The odds of having teeth with untreated coronal or root caries increased with the length of time since the last routine appointment. Compared to those who had a dental appointment within a year, individuals who had their last dental appointment more than 3 years ago had 0.44 times the odds of having missing teeth among routine users (95%CI = 0.33, 0.59) and 0.67 times the odds among urgent users (95%CI = 0.45, 0.98). Conclusions Recent routine dental appointments are associated with improved oral health outcomes. Disparities exist in access to care for low-income and/or members of racial/ethnic minorities. The outcomes reiterate how social determinants of health impact access to oral health care and subsequent oral health outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".