A Delphi study to identify relevant scenarios as the first step toward an international hyperbaric medicine simulation curriculum
Bibliographic record
Abstract
INTRODUCTION: Evidence across healthcare specialties suggests that simulation-based education improves practices and patient outcomes. However, simulation has yet to be widely used in hyperbaric medicine education. We aimed to identify the most relevant clinical scenarios for inclusion in a simulation-based curriculum for hyperbaric medicine. METHODS: After ethics approval, we used a modified Delphi consensus method. We assembled an initial questionnaire and distributed it online in English and French to an international group of hyperbaric physicians and operators using a snowball recruitment technique. Participants rated the list of scenarios using a 5-point scale ranging from 1 (least relevant) to 5 (most relevant). Scenarios judged by at least 80% of participants to be relevant (score 4 or 5) were automatically included. Scenarios that did not meet this threshold and new scenarios suggested by participants during the first round were included in a second round. RESULTS: Seventy-one participants from nine countries, including both physicians and non-physicians, completed the first round and 34 completed the second. Five scenarios were identified as relevant: seizure, fire, cardiac arrest, pneumothorax, and technical deficiency such as power loss while operating the chamber. CONCLUSIONS: Five scenarios relevant for inclusion in the simulation-based curriculum in hyperbaric medicine were identified by expert consensus.
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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 | 0.094 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".