Identification of Sleep Medicine and Anesthesia Core Topics for Anesthesia Residency: A Modified Delphi Technique Survey
Bibliographic record
Abstract
BACKGROUND: Sleep disorders affect up to 25% of the general population and are associated with increased risk of adverse perioperative events. The key sleep medicine topics that are most important for the practice of anesthesiology have not been well-defined. The objective of this study was to determine the high-priority sleep medicine topics that should be included in the education of anesthesia residents based on the insight of experts in the fields of anesthesia and sleep medicine. METHODS: We conducted a prospective cross-sectional survey of experts in the fields of sleep medicine and anesthesia based on the Delphi technique to establish consensus on the sleep medicine topics that should be incorporated into anesthesia residency curricula. Consensus for inclusion of a topic was defined as >80% of all experts selecting "agree" or "strongly agree" on a 5-point Likert scale. Responses to the survey questions were analyzed with descriptive statistical methods and presented as percentages or weighted mean values with standard deviations (SD) for Likert scale data. RESULTS: The topics that were found to have 100% agreement among experts were the influence of opioids and anesthetics on control of breathing and upper airway obstruction; potential interactions of wake-promoting/hypnotic medications with anesthetic agents; effects of sleep and anesthesia on upper airway patency; and anesthetic management of sleep apnea. Less than 80% agreement was found for topics on the anesthetic implications of other sleep disorders and future pathways in sleep medicine and anesthesia. CONCLUSIONS: We identify key topics of sleep medicine that can be included in the future design of anesthesia residency training curricula.
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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 | 0.029 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".