A dignitary medicine curriculum developed using a modified Delphi methodology
Bibliographic record
Abstract
BACKGROUND: Dignitary medicine is an emerging field of training that involves the specialized care of diplomats, heads of state, and other high-ranking officials. In an effort to provide guidance on training in this nascent field, we convened a panel of experts in dignitary medicine and using the Delphi methodology, created a consensus curriculum for training in dignitary medicine. METHODS: A three-round Delphi consensus process was performed with 42 experts in the field of dignitary medicine. Predetermined scores were required for an aspect of the curriculum to advance to the next round. The scores on the final round were used to determine the components of the curriculum. Scores below the threshold to advance were dropped in the subsequent round. RESULTS: Our panel had a high degree of agreement on the required skills needed to practice dignitary medicine, with active practice in a provider's baseline specialty, current board certification, and skills in emergency care and resuscitation being the highest rated skills dignitary medicine physicians need. Skills related to vascular and emergency ultrasound and quality improvement were rated the lowest in the Delphi analysis. No skills were dropped from consideration. CONCLUSIONS: The results of our work can form the basis of formal fellowship training, continuing medical education, and publications in the field of dignitary medicine. It is clear that active medical practice and knowledge of resuscitation and emergency care are critical skills in this field, making emergency medicine physicians well suited to practicing dignitary medicine.
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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 | 0.071 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".