Safety of outpatient non-upper airway surgery for patients with obstructive sleep apnea in ambulatory surgical centers: A systematic review
Bibliographic record
Abstract
OBJECTIVE: The systematic review aims to determine the safety of conducting non-upper airway surgery in an ambulatory surgery center (ASC) for OSA patients. DATA SOURCES: A comprehensive search was conducted from MEDLINE, Embase, CENTRAL, and Scopus from inception through February 2023. REVIEW METHODS: Studies including non-upper airway surgery done in ASC settings were identified. Risk of bias was assessed using the Murad Tool and Newcastle-Ottawa scale. Primary outcomes were 24 hour complications and unplanned admission rates. RESULTS: From 9313 studies, 13 non-OSA studies with 31,200 OSA participants and 318,709 non-OSA participants were identified. Severe complications were rare and tended to occur within the first 4 hours of the postoperative period. While a majority of smaller scale studies found no significant difference in unplanned admissions, large scale studies with multivariate analysis find OSA to be an independent risk factor for unplanned admission and 30-day complications. However, large scale ASC studies have found that with proper selection and perioperative interventions, OSA patients can undergo outpatient surgery at ASCs safely. CONCLUSIONS: OSA patients with mild or controlled comorbidities can safely undergo ambulatory non-OSA surgery in ASCs. OTHER: The protocol for this review was registered with the PROSPERO database (Registration number: CRD42023415162).
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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 | 0.009 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".