Teaching New Materials and Techniques for Fixed Dental Prostheses in Dental Schools in the United States and Canada: A Survey
Bibliographic record
Abstract
PURPOSE: Tooth-supported fixed dental prostheses (FDPs) remain an extensive therapeutic option, with new materials and a digital fabrication process gradually replacing traditional methods. The variety of advances calls for dental schools to expand the teaching of this field. Our aim was to examine the nature and the amount of theoretical and practical training in clinical FDP courses, and the extent to which new methods and innovations in the field are being integrated. MATERIALS AND METHODS: Data were collected using an online questionnaire including theoretical and practical teaching regarding clinical courses, restorative materials, and new fabrication methods, which was sent to 58 dental schools in North America. RESULTS: A total of 36 schools responded to the survey for a response rate of 62.1%. All the schools teach theoretical and practical porcelain-fused-to-metal (PFM) restorations, and almost all the schools teach full-metal FDPs. In more than half (57.1%) of the schools, zirconia-based FDPs are placed by students. Students place partial veneer FDPs in less than one-fifth (17.9%) of the schools. The average number of restorations required for completion of the clinical course is 7.3 FDP units. The respondents assessed that of the total FDPs placed by students in the clinical course, tooth-color coping (zirconia/alumina/porcelain) FDPs constituted 16.2%. None of the schools produce computer-assisted design/computer-assisted manufacturing copings or crowns within the school. CONCLUSIONS: All North American dental schools include teaching and placement of PFM restorations in their curriculum, but only one-third teach ceramic-based crowns. The low average number of required crowns may lead to graduates not being exposed to and trained in up-to-date dental restorative materials and techniques.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".