Teaching Vital Pulp Treatment for Permanent Teeth to Undergraduate/Pre‐Doctoral Students: A Multinational Survey
Bibliographic record
Abstract
AIM: To investigate the current status of education in vital pulp treatment (VPT) for the management of permanent teeth in relation to undergraduate/pre-doctoral students at a range of dental schools worldwide. METHODOLOGY: The web-based survey consisted of 28 questions that had been validated and piloted by a range of experienced endodontists. Faculty members who taught endodontics at one dental school in each selected country participated in the survey, which was completed in March 2025. Simple descriptive statistics were used to present the data. RESULTS: Forty faculty members from various countries completed the survey; the majority of which (82.5%) worked at public universities. All participating dental schools included VPT teaching in their undergraduate/predoctoral curricula. The primary method of teaching VPT in the majority of dental schools was didactic lectures. VPT preclinical exercises were included in only 32.5% of the schools. The vast majority of dental schools did not require students to pass a preclinical or clinical competency examination in relation to VPT prior to graduation. Hydraulic calcium silicate materials were the most commonly used for exposed pulps but not for pulps that were not exposed. CONCLUSIONS: VPT has been integrated into the undergraduate/pre-doctoral curriculum of all dental schools that participated in this survey. However, the majority of dental schools that were surveyed lacked preclinical teaching and competency assessments for VPT in both preclinical and clinical scenarios.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".