Development of a Core Curriculum Framework in Cariology for U.S. Dental Schools
Bibliographic record
Abstract
Maintenance of health and preservation of tooth structure through risk-based prevention and patient-centered, evidence-based disease management, reassessed at regular intervals over time, are the cornerstones of present-day caries management. Yet management of caries based on risk assessment that goes beyond restorative care has not had a strong place in curriculum development and competency assessment in U.S. dental schools. The aim of this study was to develop a competency-based core cariology curriculum framework for use in U.S. dental schools. The Section on Cariology of the American Dental Education Association (ADEA) organized a one-day consensus workshop, followed by a meeting program, to adapt the European Core Cariology Curriculum to the needs of U.S. dental education. Participants in the workshop were 73 faculty members from 35 U.S., three Canadian, and four international dental schools. Representatives from all 65 U.S. dental schools were then invited to review and provide feedback on a draft document. A recommended competency statement on caries management was also developed: "Upon graduation, a dentist must be competent in evidence-based detection, diagnosis, risk assessment, prevention, and nonsurgical and surgical management of dental caries, both at the individual and community levels, and be able to reassess the outcomes of interventions over time." This competency statement supports a curriculum framework built around five domains: 1) knowledge base; 2) risk assessment, diagnosis, and synthesis; 3) treatment decision making: preventive strategies and nonsurgical management; 4) treatment decision making: surgical therapy; and 5) evidence-based cariology in clinical and public health practice. Each domain includes objectives and learning outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".