Home-use servo-ventilation therapy in chronic pain patients with central sleep apnea: initial and 3-month follow-up
Bibliographic record
Abstract
PURPOSE: Opioid treatment of non-malignant chronic pain can result in hypoxemia, hypercarbia, and central sleep apnea. The aim of this study was to determine the initial efficacy of auto servo-ventilation (ASV) and after 3 months of home use. METHODS: This prospective multicenter interventional study recruited chronic pain patients prescribed ≥100 morphine equivalents for at least 4 months. PARTICIPANTS: Following full-night polysomnography (PSG) to confirm the presence of sleep-disordered breathing, patients were randomized to three additional full-night-attended PSGs with continuous positive airway pressure (CPAP), ASV, and servo-ventilation with an initial mandatory pressure support of 6 cm H2O (ASV manual PSmin 6). Following the PSGs, patients were sent home with EncoreAnywhere and ASV with or without mandatory pressure support. RESULTS: Based on the initial PSG studies, CPAP improved but did not normalize the apnea-hypopnea index (AHI), central apnea index (CAI), or hypopnea index (HI), as all remained elevated. Clinically significant reductions were noted after just one night of ASV and ASV manual (PSmin 6). After 3 months of ASV home use, the AHI, CAI, and obstructive apnea index (OAI) were significantly reduced when compared to baseline diagnostic levels and even when compared to respiratory disturbance indices with CPAP treatment. CONCLUSIONS: Initial and home use of ASV for 3 months resulted in significantly lower AHI, CAI, and OAI. This reduction attests to the efficacy of ASV treatment in chronic pain patients on high doses of opioids.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".