An international survey in postgraduate training in Oral Medicine
Bibliographic record
Abstract
OBJECTIVES: The aim of this preliminary study was to investigate postgraduate Oral Medicine training worldwide and to begin to identify minimum requirements and/or core content for an International Oral Medicine curriculum. MATERIALS AND METHODS: Countries where there was believed to be postgraduate training in Oral Medicine were identified by the working group. Standardized emails were sent inviting participants to complete an online survey regarding the scope of postgraduate training in Oral Medicine in their respective countries. RESULTS: We received 69 total responses from 37 countries. Of these, 22 countries self-identified as having postgraduate Oral Medicine as a distinct field of study, and they served as the study group. While there is currently considerable variation among Oral Medicine postgraduate training parameters, there is considerable congruency in clinical content of the Oral Medicine syllabi. For example, all of the training programs responded that they did evaluate competence in diagnosis and management of oral mucosal disease. CONCLUSIONS: This preliminary study provides the first evidence regarding international Oral Medicine postgraduate training, from which recommendations for an international core curriculum could be initiated. It is through such an initiative that a universal clinical core syllabus in postgraduate Oral Medicine training may be more feasible.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".