Perioperative management for the obese outpatient
Bibliographic record
Abstract
PURPOSE OF REVIEW: Incidence of obesity continues to rise and ambulatory surgical centers will need to be prepared for the increase in the obese surgical patients. This review aims to provide recent updates in managing the obese patients in an ambulatory surgical center and to address key clinical questions, such as patient selection, assessment and optimization, as well as important perioperative consideration. RECENT FINDINGS: With low rate of major intraoperative adverse events, obesity has not been associated with unplanned admission after day surgical procedures. There is, however, a higher rate of perioperative adverse events in the super-obese patients. Recent developments in patient assessment include validation of STOP-Bang questionnaire for obstructive sleep apnea in the obese population. Nevertheless, patients with obesity hypoventilation syndrome should be identified and optimized as they are more prone to develop adverse events. The obese patients are also at a higher risk of difficult airway, and recommendations for the airway management are available. SUMMARY: With extra considerations and meticulous perioperative management, it is well tolerated to accept obese patients for ambulatory surgery. The super-obese patients, however, are at a higher risk for perioperative adverse events.
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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) | 0.001 | 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) | 0.004 | 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".