Assessment of Digital Workflow in Predoctoral Education and Patient Care in North American Dental Schools
Bibliographic record
Abstract
Technology has revolutionized the field of dentistry, and digital workflow has become commonplace in everyday dental practices. However, are future practitioners prepared to enter into an increasingly digitized world? The aim of this study was to determine the extent to which digital modalities were being taught to predoctoral dental students and used for patient care in dental schools throughout North America. A 35-question survey was sent in February 2019 to all 76 dental schools in the U.S. and Canada. After 90 days, 54 recorded responses were received, for a 71% response rate. Students were reported to be using CAD/CAM technology in 50 (93%) of the 54 responding schools. While almost all schools responding to the survey were using digital scanning, there was disparity among them in terms of the types and frequency of procedures for which digital impressions were utilized. This study found that the incorporation of CAD/CAM technology in predoctoral dental curricula varied widely. However, it was clear that the relative dearth of well-trained faculty members and the number of CAD/CAM units available to students limited its use. It is imperative that more emphasis is placed on the utilization of digital workflow in North American dental schools for future practitioners to practice contemporary restorative dentistry.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".