Obesity hypoventilation syndrome, sleep apnea, overlap syndrome
Bibliographic record
Abstract
PURPOSE OF REVIEW: The prevalence of sleep disordered breathing (SDB) is increasing proportional to the prevalence of obesity. Although anesthesiologists are familiar with obstructive sleep apnea (OSA) - the most common SDB, anesthesiologists may not be aware of other SDB such as obesity hypoventilation syndrome (OHS) and overlap syndrome (combination of OSA and chronic obstructive pulmonary disease). The present review provides an update of information regarding the perioperative management of OHS and overlap syndrome. RECENT FINDINGS: OHS and overlap syndrome are associated with significant comorbid conditions and more perioperative morbidity than OSA alone. Similar to OSA, most of the OHS patients are undiagnosed. An increase in serum bicarbonate level is a surrogate marker of hypercapnia. Because 90% of OHS patients have OSA, preoperative screening for OSA combined with estimation of serum bicarbonate level may detect the majority of the patients with OHS. In patients with OSA, OHS, and overlap syndrome, improvement in the perioperative outcome has been shown by initiating positive airway pressure therapy. SUMMARY: Identification and preoperative optimization of these high-risk patients are most important. A protocol-based risk mitigation is necessary for improving the intraoperative and postoperative outcome of these patients. As a perioperative physician, anesthesiologists have a key role in the management of patients with SDB.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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".