Nocturnal haemodialysis increases pharyngeal size in patients with sleep apnoea and end-stage renal disease
Bibliographic record
Abstract
BACKGROUND: Sleep apnoea is common in patients with end-stage renal disease (ESRD) and is improved by nocturnal haemodialysis (NHD). Recent findings from our laboratory indicate the development of ESRD is associated with pharyngeal narrowing. We hypothesized that NHD increases pharyngeal cross-sectional area and that this is associated with an improvement in sleep apnoea. METHODS: Twenty-four patients (aged 32-68 years), receiving conventional haemodialysis (CHD) (4 h/day, 3 days/week), were recruited for overnight polysomnography and estimation of pharyngeal cross-sectional area at functional residual capacity (FRC) and residual volume (RV). Patients were divided into apnoeic and non-apnoeic groups based on an apnoea-hypopnoea index (AHI) > or = 15/h. Following conversion from CHD to NHD (8 h/night, 3-6 nights/week) all measurements were repeated and apnoeic patients were classified as 'responders' if AHI fell to < 15 events/h. RESULTS: Conversion from CHD to NHD was associated with an increase in pharyngeal cross-sectional area (FRC: 3.29 +/- 0.67 vs 3.39 +/- 0.75 cm(2); RV: 1.91 +/- 0.51 vs 2.13 +/- 0.48 cm(2), P < 0.05), which was not significantly different between groups. Sleep apnoea improved in three patients. CONCLUSIONS: Conversion from CHD to NHD is associated with an increase in pharyngeal cross-sectional area. This may play a role in some patients whose sleep apnoea improves on NHD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".