Predictive factors for sleep apnoea in patients on opioids for chronic pain
Bibliographic record
Abstract
Background The risk of death is elevated in patients taking opioids for chronic non-cancer pain. Respiratory depression is the main cause of death due to opioids and sleep apnoea is an important associated risk factor. Methods In chronic pain clinics, we assessed the STOP-Bang questionnaire (a screening tool for sleep apnoea; S noring, T iredness, O bserved apnoea, high blood P ressure, B ody mass index, age, neck circumference and male gender), Epworth Sleepiness Scale, thyromental distance, Mallampati classification, daytime oxyhaemoglobin saturation (SpO 2 ) and calculated daily morphine milligram equivalent (MME) approximations for each participant, and performed an inlaboratory polysomnogram. The primary objective was to determine the predictive factors for sleep apnoea in patients on chronic opioid therapy using multivariable logistic regression models. Results Of 332 consented participants, 204 underwent polysomnography, and 120 (58.8%) had sleep apnoea (AHI ≥5) (72% obstructive, 20% central and 8% indeterminate sleep apnoea), with a high prevalence of moderate (23.3%) and severe (30.8%) sleep apnoea. The STOP-Bang questionnaire and SpO 2 are predictive factors for sleep apnoea (AHI ≥15) in patients on opioids for chronic pain. For each one-unit increase in the STOP-Bang score, the odds of moderate-to-severe sleep apnoea (AHI ≥15) increased by 70%, and for each 1% SpO 2 decrease the odds increased by 33%. For each 10 mg MME increase, the odds of Central Apnoea Index ≥5 increased by 3%, and for each 1% SpO 2 decrease the odds increased by 45%. Conclusion In patients on opioids for chronic pain, the STOP-Bang questionnaire and daytime SpO 2 are predictive factors for sleep apnoea, and MME and daytime SpO 2 are predictive factors for Central Apnoea Index ≥5. Trial registration number NCT02513836
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".