Teaching Methods and Surgical Training in North American Graduate Periodontics Programs: Exploring the Landscape
Bibliographic record
Abstract
This project aimed at documenting the surgical training curricula offered by North American graduate periodontics programs. A survey consisting of questions on teaching methods employed and the content of the surgical training program was mailed to directors of all fifty-eight graduate periodontics programs in Canada and the United States. The chi-square test was used to assess whether the residents' clinical experience was significantly (P<0.05) influenced by having a) a structured preclinical program or b) another dental residency program in the institution. Thirty-four programs (59 percent) responded to the survey. Twenty-six programs (76 percent of respondents) reported offering a structured preclinical component. Traditional teaching methods such as slides, live demonstration, DVD/CD, and animal cadavers were the most common teaching methods used, whereas online courses, computer simulation, and various surgical mannequins were least commonly used. The most commonly performed surgical procedures were conventional flaps, periodontal plastic procedures, hard tissue grafts, and implants. Furthermore, residents in programs offering a structured preclinical component performed significantly more procedures (P=0.012) using lasers than those in programs not offering a structured preclinical program. Devising new and innovative teaching methods is a clear avenue for future development in North American graduate periodontics programs.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".