Periodontal‐Systemic Disease Education in U.S. and Canadian Dental Schools
Bibliographic record
Abstract
Research has proliferated in recent years regarding the relationship of oral disease to systemic conditions. Specifically, periodontal disease has been studied as a potential risk factor for multiple conditions such as cardiovascular disease (CVD) and adverse pregnancy outcomes, while other research focuses on exposures or behaviors associated with oral disease. However, few articles have been published reporting how this information is integrated into schools of dentistry, both in the classroom and clinical curriculum. For our study, a thirty-three-item survey and cover letter were electronically mailed to academic deans at sixty-five accredited dental schools in the United States and Canada in the fall of 2007. The response rate was 77 percent. According to the responses to this survey, the primary topics covered in the didactic curriculum regarding periodontal oral-systemic disease are aging, CVD, diabetes, and tobacco use. Eighty-eight percent of the respondents reported that their students are knowledgeable about the role of inflammation and its impact on oral-systemic conditions. Forty-eight percent of the respondents said they provide formal training for their students in how to discuss or communicate aspects of periodontal oral-systemic disease with patients. Only seven schools reported teaching didactic content to dental students intermixed with other health professions students, and only two schools reported conducting joint projects. Only 9 percent of the respondents said they think nurses and physicians are knowledgeable about oral-systemic disease. The findings indicate that dental schools are confident about the knowledge of their students regarding oral-systemic content. However, much work is needed to educate dental students to work in a collaborative fashion with other health care providers to co-manage patients at risk for oral-systemic conditions.
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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".