Integrating Sleep Knowledge Into the Anesthesiology Curriculum
Bibliographic record
Abstract
There is common ground between the specialties of anesthesiology and sleep medicine. Traditional sleep medicine curriculum for anesthesiology trainees has revolved around the discussion of obstructive sleep apnea (OSA) and its perioperative management. However, it is time to include a broader scope of sleep medicine-related topics that overlap these specialties into the core anesthesia residency curriculum. Five main core competency domains are proposed, including SLeep physiology; Evaluation of sleep health; Evaluation for sleep disorders and clinical implications; Professional and academic roles; and WELLness (SLEEP WELL). The range of topics include not only the basics of the physiology of sleep and sleep-disordered breathing (eg, OSA and central sleep apnea) but also insomnia, sleep-related movement disorders (eg, restless legs syndrome), and disorders of daytime hypersomnolence (eg, narcolepsy) in the perioperative and chronic pain settings. Awareness of these topics is relevant to the scope of knowledge of anesthesiologists as perioperative physicians as well as to optimal sleep health and physician wellness and increase consideration among current anesthesiology trainees for the value of dual credentialing in both these specialties.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".