Simple screening model for identifying the risk of sleep apnea in patients on opioids for chronic pain
Bibliographic record
Abstract
Background There is an increased risk of sleep apnea in patients using opioids for chronic pain. We hypothesized that a simple model comprizing of: (1) STOP-Bang questionnaire and resting daytime oxyhemoglobin saturation (SpO2); and (2) overnight oximetry will identify those at risk of moderate-to-severe sleep apnea in patients with chronic pain. Method Adults on opioids for chronic pain were recruited from pain clinics. Participants completed the STOP-Bang questionnaire, resting daytime SpO2, and in-laboratory polysomnography. Overnight oximetry was performed at home to derive the Oxygen Desaturation Index. A STOP-Bang score ≥3 or resting daytime SpO2 ≤95% were used as thresholds for the first step, and for those identified at risk, overnight oximetry was used for further screening. The Oxygen Desaturation Index from overnight oximetry was validated against the Apnea-Hypopnea Index (≥15 events/hour) from polysomnography. Results Of 199 participants (52.5±12.8 years, 58% women), 159 (79.9%) had a STOP-Bang score ≥3 or resting SpO2 ≤95% and entered the second step (overnight oximetry). Using an Oxygen Desaturation Index ≥5 events/hour, the model had a sensitivity of 86.4% and specificity of 52% for identifying moderate-to-severe sleep apnea. The number of participants who would require diagnostic sleep studies was decreased by 38% from Step 1 to Step 2 of the model. Conclusion A simple model using STOP-Bang questionnaire and resting daytime SpO2, followed by overnight oximetry, can identify those at high risk of moderate-to-severe sleep apnea in patients using opioids for chronic pain. Trial registration number NCT02513836 .
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".