Auto-servoventilation in heart failure with sleep apnoea: a randomised controlled trial
Bibliographic record
Abstract
We tested the hypotheses that in patients with congestive heart failure (CHF) and sleep disordered breathing (SDB) auto-servoventilation (ASV) improves cardiac function and quality of life. Between March 2007 and September 2009, patients with stable CHF (left ventricular ejection fraction (LVEF) ≤ 40%) and SDB (apnoea/hypopnoea index ≥ 20 events · h(-1)) were randomised to receive either ASV (BiPAP ASV (Philips Respironics, Murrysville, PA, USA), n=37) and optimal medical management, or optimal medical management alone (n=35). Outcomes were assessed at baseline and 12 weeks. The apnoea/hypopnoea index assessed with polysomnography scored in one core laboratory was significantly more reduced in the ASV group (-39 ± 16 versus -1 ± 13 events · h(-1); p<0.001) with an average use of 4.5 ± 3.0 h · day(-1). Both groups showed similar improvements of the primary end-point LVEF (+3.4 ± 5 versus +3.5 ± 6%; p=0.915) assessed with echocardiography. In the ASV group, reduction of N-terminal pro-brain natriuretic peptide (NT-proBNP) was significantly greater (-360 ± 569 versus +135 ± 625 ng · mL(-1); p=0.010). No differences were observed between the groups in subjective quality of life. In patients with CHF and SDB, ASV reduced NT-proBNP levels, but improvement of LVEF or quality of life was not greater than in the control group. The data support that such patients can be randomised in large-scale, long-term trials of positive airway pressure therapy versus control to determine effects on cardiovascular outcome.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".