Preoxygenation and intraoperative ventilation strategies in obese patients
Bibliographic record
Abstract
PURPOSE OF REVIEW: Obesity along with its pathophysiological changes increases risk of intraoperative and perioperative respiratory complications. The aim of this review is to highlight recent updates in preoxygenation techniques and intraoperative ventilation strategies in obese patients to optimize gas exchange and pulmonary mechanics and reduce pulmonary complications. RECENT FINDINGS: There is no gold standard in preoxygenation or intraoperative ventilatory management protocol for obese patients. Preoxygenation in head up or sitting position has been shown to be superior to supine position. Apneic oxygenation and use of continuous positive airway pressure increases safe apnea duration. Recent evidence encourages the intraoperative use of low tidal volume to improve oxygenation and lung compliance without adverse effects. Contrary to nonobese patients, some studies have reported the beneficial effect of recruitment maneuvers and positive end-expiratory pressure in obese patients. No difference has been observed between volume controlled and pressure controlled ventilation. SUMMARY: The ideal ventilatory plan for obese patients is indeterminate. A multimodal preoxygenation and intraoperative ventilation plan is helpful in obese patients to reduce perioperative respiratory complications. More studies are needed to identify the role of low tidal volume, positive end-expiratory pressure, and recruitment maneuvers in obese patients undergoing general anesthesia.
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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) | 0.003 | 0.000 |
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".