The Teaching of Personalized Dentistry in North American Dental Schools: Changes from 2014 to 2017
Bibliographic record
Abstract
The aim of this study was to assess the development of personalized dentistry in the curricula of North American dental schools from 2014 to 2017. In 2014, a web-based survey on personalized medicine/dentistry (PM/PD) was distributed to academic deans of all U.S. (n=65) and Canadian (n=10) dental schools with graduating classes. The results (n=42; 56% response rate) showed that few schools had plans for implementation of PM/PD at the time, even though the majority of respondents reported feeling that PM/PD should be taught in the curriculum and will impact clinical practice in the future. A three-year followup survey in 2017, sent to the same 75 schools, was designed to reassess the teaching/practice of PM/PD in dental schools in both didactic and clinical curricula. In the results of the 2017 survey (n=30; 40% response rate), the majority of respondents reported feeling that PM/PD should be taught in dental curricula. However, while most respondents indicated their schools did not teach PM/PD as a portion of their didactic curricula, they reported that specific pertinent PM/PD topics were taught as part of other courses in their curricula. The 2017 survey also evaluated the use of seven genetics-based and eight non-genetics-based PM/PD diagnostics in the schools' clinical curricula. Overall, non-genetics-based diagnostics were used more often than genetics-based diagnostics, and the use of genetics-based diagnostics was more prevalent in postgraduate than predoctoral clinics. Personalized dentistry will inevitably be part of the dental professional's future and should be reflected in basic science research, clinical settings, and dental school curricula in both predoctoral and postgraduate programs.
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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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".