Effects of exercise training on sleep apnoea in patients with coronary artery disease: a randomised trial
Bibliographic record
Abstract
Overnight fluid shift from the legs to the neck and lungs may contribute to the pathogenesis of obstructive sleep apnoea (OSA) and central sleep apnoea (CSA). We hypothesised that exercise training will decrease the severity of OSA and CSA in patients with coronary artery disease (CAD) by decreasing daytime leg fluid accumulation and overnight rostral fluid shift.Patients with CAD and OSA or CSA (apnoea-hypopnoea index >15 events per h) were randomised to 4 weeks of aerobic exercise training or to a control group. Polysomnography, with measurement of leg, thoracic and neck fluid volumes and upper-airway cross-sectional area (UA-XSA) before and after sleep, was performed at baseline and follow-up.17 patients per group completed the study. Apnoea-hypopnoea index decreased significantly more in the exercise group than in the control group (31.1±12.9 to 20.5±9.4 versus 28.1±13.5 to 27.0±15.1 events per h, p=0.047), in association with a greater reduction in the overnight change in leg fluid volume (579±222 to 466±163 versus 453±164 to 434±141 mL, p=0.04) and by a significantly greater increase in the overnight change in UA-XSA in the exercise group (p=0.04).In patients with CAD and sleep apnoea, exercise training decreases sleep apnoea severity via attenuation of overnight fluid shift and an increase in UA-XSA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".