STOP-Bang Score and Prediction of Severity of Obstructive Sleep Apnea in a First Nation Community in Saskatchewan, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The STOP-Bang questionnaire is an easy-to-administer scoring model to screen and identify patients at high risk of obstructive sleep apnea (OSA). However, its diagnostic utility has never been tested with First Nation peoples. The objective was to determine the predictive parameters and the utility of the STOP-Bang questionnaire as an OSA screening tool in a First Nation community in Saskatchewan. The baseline survey of the First Nations Sleep Health Project (FNSHP) was completed between 2018 and 2019. Of the available 233 sleep apnea tests, 215 participants completed the STOP-Bang score questionnaire. A proportional odds ordinal logistic regression analysis was conducted using the total score of the STOP-Bang as the independent variable with equal weight given to each response. Predicted probabilities for each score at cut-off points of the Apnea Hypopnea Index (AHI) were calculated and plotted. To assess the performance of the STOP-Bang questionnaire, sensitivity, specificity, positive predictive values (PPVs), negative predictive values (NPVs), and area under the curve (AUC) were calculated. These data suggest that a STOP-Bang score ≥ 5 will allow healthcare professionals to identify individuals with an increased probability of moderate-to-severe OSA, with high specificity (93.7%) and NPV (91.8%). For the STOP-Bang score cut-off ≥ 3, the sensitivity was 53.1% for all OSA and 72.0% for moderate-to-severe OSA. For the STOP-Bang score cut-off ≥ 3, the specificity was 68.4% for all OSA and 62.6% for moderate-to-severe OSA. The STOP-Bang score was modestly superior to the symptom of loud snoring, or loud snoring plus obesity in this population. Analysis by sex suggested that a STOP-Bang score ≥ 5 was able to identify individuals with increased probability of moderate-to-severe OSA, for males with acceptable diagnostic test accuracy for detecting participants with OSA, but there was no diagnostic test accuracy for females.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it