An update on the various practical applications of the STOP-Bang questionnaire in anesthesia, surgery, and perioperative medicine
Bibliographic record
Abstract
PURPOSE OF REVIEW: The present review aims to provide an update on the various practical applications of the STOP-Bang questionnaire in anesthesia, surgery, and perioperative medicine. RECENT FINDINGS: The STOP-Bang questionnaire was originally validated as a screening tool to identify surgical patients who are at high-risk of obstructive sleep apnea (OSA). A recent meta-analysis confirmed that STOP-Bang is validated for use in the sleep clinic, surgical, and general population. Patients with a STOP-Bang score of 0--2 can be classified as low-risk for moderate-to-severe OSA. Those with a score of 5--8 can be classified as high-risk for moderate-to-severe OSA. In patients with a score of 3 or 4, a specific combination of a STOP score at least 2 + BMI more than 35 kg/m or STOP score at least 2 + male or STOP score at least 2 + neck circumference more than 40 cm indicates higher risk for moderate-to-severe OSA. Further, patients with a STOP-Bang score at least 3 can be classified as high risk for moderate-to-severe OSA if the serum HCO3 at least 28 mmol/l. STOP-Bang can be used as a novel tool for perioperative risk stratification because it easily identifies patients who are at increased risk of perioperative complications. SUMMARY: STOP-Bang at least 3 was recommended previously to identify the suspected or undiagnosed OSA. To reduce the false positive cases and to improve its specificity, a stepwise stratification is recommended to identify the patients at high risk of moderate-to-severe OSA. Because of its practical application, STOP-Bang is a useful screening tool for patients with suspected or undiagnosed OSA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".