An update on preoperative assessment and preparation of surgical patients with obstructive sleep apnea
Bibliographic record
Abstract
PURPOSE OF REVIEW: There is a high prevalence of obstructive sleep apnea (OSA) in the surgical population, however, a significant proportion of patients are undiagnosed. The Society of Anesthesia and Sleep Medicine (SASM) has issued recent guidelines for preoperative assessment and preparation of patients with known or suspected OSA. The purpose of this review is to highlight key points in the new guidelines and explore the possibilities of different strategies in optimizing patients with OSA preoperatively. RECENT FINDINGS: Recent knowledge on phenotypes and endotypes has provided a better understanding of the disease and its underlying pathogenesis. Phenotypes refer to the predominant morphological characteristics of an individual whereas endotypes refer to the predominant underlying mechanism of the disease. Phenotypes and endotypes in OSA are heterogenous. Heterogeneity in the pathogenic mechanisms implies that opportunities other than the use of continuous positive airway pressure (CPAP) may exist to optimize or manage OSA patients preoperatively. SUMMARY: The prevalence of OSA in surgical patients is high. SASM has made recommendations in their published guidelines for the optimum preoperative preparation of patients with OSA. In the future, research may shift towards finding the underlying mechanism of OSA for targeted therapy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".