A Survey of Dental Implant Instruction in Predoctoral Dental Curricula in North America
Bibliographic record
Abstract
The aim of this study was to investigate the extent and forms of predoctoral implant dentistry instruction in North American dental schools and to identify future directions and challenges. The survey collected data on curriculum content, departmental oversight, techniques, and materials, as well as current problems to be solved. The 30-question survey was sent in 2012 to the dean or administrator in charge of the predoctoral curriculum of all 73 dental schools in the U.S. and Canada at the time; four reminders were sent. Forty-seven schools responded, for a response rate of 64%. Of the 47 responding schools, 46 (98%) offered didactic instruction (mean of 17 hours); 87% had a laboratory component (mean of 14.46 hours); and 57% had a clinical requirement. In the responding schools, students had an average of 1.85 implant restorative cases and 0.61 surgical cases. Forty-two of the schools (89%) had implemented observation of implant surgery and/or assisting with implant surgery in their curricula. Major challenges reported in implementing a comprehensive predoctoral implant curriculum included expense of implant systems to the schools and to patients, shortage of predoctoral cases, and lack of curriculum time and trained faculty. These results show that implant education for predoctoral dental students continues to expand, with a trend towards more preclinical exercises and clinical experiences and fewer didactic courses.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".