High STOP-Bang score indicates a high probability of obstructive sleep apnoea
Bibliographic record
Abstract
BACKGROUND: The STOP-Bang questionnaire is used to screen patients for obstructive sleep apnoea (OSA). We evaluated the association between STOP-Bang scores and the probability of OSA. METHODS: After Institutional Review Board approval, patients who visited the preoperative clinics for a scheduled inpatient surgery were approached for informed consent. Patients answered STOP questionnaire and underwent either laboratory or portable polysomnography (PSG). PSG recordings were scored manually. The BMI, age, neck circumference, and gender (Bang) were documented. Over 4 yr, 6369 patients were approached and 1312 (20.6%) consented. Of them, 930 completed PSG, and 746 patients with complete data on PSG and STOP-Bang questionnaire were included for data analysis. RESULTS: The median age of 746 patients was 60 yr, 49% males, BMI 30 kg m(-2), and neck circumference 39 cm. OSA was present in 68.4% with 29.9% mild, 20.5% moderate, and 18.0% severe OSA. For a STOP-Bang score of 5, the odds ratio (OR) for moderate/severe and severe OSA was 4.8 and 10.4, respectively. For STOP-Bang 6, the OR for moderate/severe and severe OSA was 6.3 and 11.6, respectively. For STOP-Bang 7 and 8, the OR for moderate/severe and severe OSA was 6.9 and 14.9, respectively. The predicted probabilities for moderate/severe OSA increased from 0.36 to 0.60 as the STOP-Bang score increased from 3 to 7 and 8. CONCLUSIONS: In the surgical population, a STOP-Bang score of 5-8 identified patients with high probability of moderate/severe OSA. The STOP-Bang score can help the healthcare team to stratify patients for unrecognized OSA, practice perioperative precautions, or triage patients for diagnosis and treatment.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".