Validation of the STOP-Bang questionnaire as a preoperative screening tool for obstructive sleep apnea: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Obstructive sleep apnea (OSA) is a common disorder that is highly associated with postoperative complications. The STOP-Bang questionnaire is a simple screening tool for OSA. The objective of this systematic review and meta-analysis is to evaluate the validity of the STOP-Bang questionnaire for screening OSA in the surgical population cohort. METHODS: A systematic search of the following databases was performed from 2008 to May 2021: MEDLINE, Medline-in-process, Embase, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, PsycINFO, Journals @ Ovid, Web of Science, Scopus, and CINAHL. Continued literature surveillance was performed through October 2021. RESULTS: with 47.4% male. The prevalence of all, moderate-to-severe, and severe OSA were 65.2, 37.7, and 17.0%, respectively. The pooled sensitivity of the STOP-Bang questionnaire for all, moderate-to-severe, and severe OSA was 85, 88, and 90%, and the pooled specificities were 47, 29, and 27%, respectively. The area under the curve for all, moderate-to-severe, and severe OSA was 0.84, 0.67, and 0.63. CONCLUSIONS: In the preoperative setting, the STOP-Bang questionnaire is a valid screening tool to detect OSA in patients undergoing surgery, with a high sensitivity and a high discriminative power to reasonably exclude severe OSA with a negative predictive value of 93.2%. TRIAL REGISTRATION: PROSPERO registration CRD42021260451 .
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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 | 0.030 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".