A Systemic Review of Obstructive Sleep Apnea and Its Implications for Anesthesiologists
Bibliographic record
Abstract
BACKGROUND: Obstructive sleep apnea (OSA) is present in a significant proportion of the population, but the majority of patients remain undiagnosed. It is crucial that anesthesiologists and surgeons recognize the increased perioperative risks associated with undiagnosed OSA. We present a systematic review of the literature on the perioperative management of surgical patients with OSA. METHODS: The scope of this review is restricted to publications in all surgical specialties and in the adult patient population. The main search key words were: "perioperative care," "sleep apnea," "obstructive sleep apnea," "perioperative risk," and "perioperative care." The databases Medline, Embase, Biological Abstract, Science Citation Index, and Healthstar were searched for relevant English language articles from 1966 to March 2007. RESULTS: The literature supports an increased perioperative risk in OSA patients. The American Society of Anesthesiologists guidelines support the routine screening for OSA during preoperative assessment, and methods of OSA screening are discussed in this review. This review suggests a number of perioperative management strategies to reduce surgical risk in patients with OSA. However, apart from the consensus-based American Society of Anesthesiologists guidelines, it is important to note that evidence-based recommendations are lacking in the literature. CONCLUSIONS: This review suggests ways to screen for OSA in the preoperative setting and proposes perioperative management strategies. The ultimate goal is to reduce the perioperative risk of OSA patients but, to realize that goal, research will be needed to determine whether screening for OSA and/or adapting specific perioperative management approaches translates into a lessening of adverse events in surgical patients with undiagnosed OSA.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".