Screening for obstructive sleep apnea before surgery: why is it important?
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this article is to review the screening tools available in the preoperative clinic for patients at risk of obstructive sleep apnea. RECENT FINDINGS: Obstructive sleep apnea (OSA) is the most prevalent sleep disorder. An estimated 82% of men and 92% of women with moderate-to-severe sleep apnea have not been diagnosed. Patients with undiagnosed OSA may have increased perioperative complications. The perioperative risk of patients with OSA may be reduced by appropriate screening to detect undiagnosed OSA in patients. The snoring (S), tiredness (T) during daytime, observed apnea (O), and high blood pressure (P) (STOP) questionnaire is a concise and easy-to-use screening tool to identify patients with a high risk of OSA. It has been validated in surgical patients at preoperative clinics as a screening tool. Incorporating BMI, age, neck size and gender into the STOP questionnaire (STOP-Bang), will further increase the sensitivity and negative predictive value (NPV), especially for patients with moderate-to-severe OSA. SUMMARY: The STOP questionnaire is short and can be easily incorporated into routine screening of general or surgical 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".