Positive Airway Pressure in Surgical Patients with Sleep Apnea: What is the Supporting Evidence?
Bibliographic record
Abstract
Obstructive sleep apnea (OSA) is prevalent amongst surgical patients and associated with an increased incidence of perioperative complications. The gold standard treatment for moderate-to-severe OSA is positive airway pressure (PAP) therapy. Practice guidelines by the American Society of Anesthesiologists and the Society of Anesthesia and Sleep Medicine have recommended preoperative screening for OSA and consideration of initiation of PAP therapy for patients with severe OSA. These guidelines, developed mainly by the consensus of experts, highlight the adverse impact of OSA on postoperative outcomes and recommend the use of postoperative PAP in surgical patients with moderate to severe OSA. Since the development of these guidelines, there has been an increase in the number of publications regarding the efficacy of PAP therapy in surgical patients with OSA. Our review provides an update on the existing literature on the efficacy of PAP therapy in surgical patients with OSA. We focus on the postoperative complications associated with OSA, potential mechanisms leading to the increased risk of postoperative adverse events, and summarize the perioperative guidelines for the management of patients with OSA, evidence supporting perioperative PAP therapy, as well as limitations to PAP therapy and alternatives. An update on the existing literature of the efficacy of PAP therapy in surgical patients with OSA is critical to assess the impact of prior guidelines, determine when and how to effectively implement PAP therapy, and target barriers to PAP adherence in the perioperative setting.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".