Screening and Perioperative Management of Obesity Hypoventilation Syndrome
Bibliographic record
Abstract
Obesity hypoventilation syndrome (OHS) can often be underdiagnosed or misdiagnosed and has been shown to pose significant risks in perioperative situations. Patients with OHS have a higher prevalence of baseline morbid conditions like hypertension, congestive heart failure (CHF), diabetes mellitus, atrial fibrillation, and pulmonary hypertension (PH), which contribute to adverse postoperative outcomes. The potential challenges include difficult intubation and loss of airway, postoperative respiratory failure, worsening heart failure, pulmonary hypertensive crisis, and opioid-induced respiratory depression (OIRD). It is, therefore, important to screen all obese patients for obstructive sleep apnea (OSA) and OHS before elective surgical procedures. The aim of this review is to discuss the preoperative screening and evaluation and safe anesthetic and up-to-date ventilatory management of this complex group of patients. This review also intends to increase the awareness of OHS in the adult population among hospitalists, surgeons, and cardiologists who may find themselves taking care of these patients in complex multidisciplinary settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".