Identifying demographic variables related to failed dental appointments in a university hospital-based residency program.
Bibliographic record
Abstract
PURPOSE: The objective of this study was to identify characteristics of pediatric patients who failed to keep the majority of their scheduled dental appointments in a pediatric dental clinic staffed by pediatric dental residents and faculty members. METHODS: The electronic records of all patients appointed over a continuous 54 month period were analyzed. Appointment history and demographic variables were collected. The rate of failed appointments was calculated by dividing the number of failed appointments with the total number of appointments scheduled for the patient. RESULTS: There were 7,591 patients in the analyzable dataset scheduled with a total of 48,932 appointments. Factors associated with an increased rate of failed appointments included self-paying for dental care, having a resident versus a faculty member as the provider, rural residence, and adolescent aged patients. Multivariable regression models indicated self-paying patients had higher odds and rates of failed appointments than patients with Medicaid and private insurance. CONCLUSIONS: Access to care for children may be improved by increasing the availability of private and public insurance. The establishment of a dental home and its relationship to a child receiving continuous care in an institutional setting depends upon establishing a relationship with a specific dentist.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".