Admission of Patients With Obstructive Sleep Apnea Undergoing Ambulatory Surgery in Otolaryngology—Head and Neck Surgery
Bibliographic record
Abstract
Objectives: Within Otolaryngology—Head and Neck Surgery (OHNS), obstructive sleep apnea (OSA) patients are frequently encountered. To implement policies and screening measures for admission of OSA patients undergoing ambulatory surgery, actual rates of admission must first be determined. We aimed to evaluate rates and reasons for admission of OSA patients after ambulatory OHNS surgery. Methods: Retrospective chart review was undertaken of all OSA patients undergoing elective day-surgery OHNS procedures at a tertiary center from January 1, 2018 to December 31, 2019. The primary outcome measure was percentage of OSA patients admitted to hospital after ambulatory OHNS surgery. Secondary outcome measures included reasons for admission. American Society of Anesthesiologists (ASA) score, perioperative complications, and patient demographics were captured. Results: There were 118 OSA patients, out of 1942 cases performed during the review period. Thirty-eight were excluded as the procedures were not considered ambulatory. The remaining 80 OSA patients were included for analysis, with an average age of 51.7, SD 13.8, and 30 (38%) females. The admission rate was 47.5% (38/80 patients). Admitted patients were older ( P = .0061), and had higher ASA ( P = .039). Indication for surgery or type of surgery did not differ among admitted and non-admitted patients. The majority of patients, 97% (37/38 patients), were admitted for post-operative monitoring. Conclusion: More than half of OSA patients did not require admission to hospital after ambulatory OHNS surgery, unaffected by indications for surgery or type of surgery. Higher ASA score and older age were found in admitted as compared to non-admitted patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".