Obstructive sleep apnea and postoperative complications among patients undergoing gynecologic oncology surgery
Bibliographic record
Abstract
OBJECTIVE: To investigate the prevalence of obstructive sleep apnea (OSA), physiological or risk factors associated with OSA, and OSA-associated postoperative complications among patients undergoing gynecologic oncology surgery. METHODS: A prospective observational study enrolled gynecologic oncology patients undergoing abdominal surgery at a center in the UK between August 2009 and January 2013. All patients underwent perioperative sleep oximetry for the diagnosis of OSA. Data assessed included the body mass index, the STOP-Bang score, the Epworth Sleepiness Scale score, the apnea-hypopnea index, and postoperative complications. Associations were determined between preoperative OSA and postoperative OSA, postoperative complications, and risk factors such as body mass index, age, STOP-Bang score, and Epworth score. RESULTS: Among 160 participants, 72 (45.0%) were obese and 80 (50.0%) had OSA. Obesity, older age (more than 65 years), and a neck circumference of 40 cm or more were significantly associated with OSA. Overall, 58 (36.3%) patients had postoperative complications; 21 (13.1%) had surgical complications and 37 (23.1%) had medical complications. Complications were not associated with OSA (P=0.612). Four (2.5%) patients died; mortality was not associated with OSA (P=0.810). CONCLUSION: OSA is common among gynecologic oncology patients. Portable sleep oximetry identifies gynecology patients who have OSA or require postoperative critical care. Obesity is associated with OSA, but OSA is not associated with postoperative complications in gynecologic oncology 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 | 0.003 |
| 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) | 0.001 | 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".