Education About Dental Erosion in U.S. and Canadian Dental Schools
Bibliographic record
Abstract
Dental erosion (DE) is a well-accepted multifactorial form of tooth wear involving acids. Due to its irreversible nature, recognizing the early signs is important to develop appropriate preventive strategies. However, its place in dental curricula remains unclear. Consensus has not been established regarding the integration of erosive tooth wear into core cariology curricula in North America. The extent to which DE is taught is questionable since etiology, risk assessment, and management are not all the same as for dental caries. The aim of this study was to survey U.S. and Canadian dental schools regarding their teaching of DE. Email invitations were sent to deans, chairs, and selected faculty members at all 76 U.S. and Canadian dental schools in 2016, asking them to either respond or forward the survey-link provided to the appropriate person in their school. Responses from the same school were combined for analysis. Respondents from 59 schools (77.6% response rate) responded to the survey, and all of them confirmed the inclusion of DE in their curricula. However, only 15.3% of respondents identified correctly all the clinical signs of DE. Although management through behavioral intervention was prioritized, diet analysis was often not a clinical requirement, and 45.8% of respondents did not teach any type of tooth wear index for monitoring. This study concluded that DE has a place in dental curricula, but whether this topic is adequately covered is questionable. There is a need to establish clearer topics and requirements emphasizing the diagnosis and management of DE, potentially in cariology curricula.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".