Relationship of pharyngeal water content and jugular volume with severity of obstructive sleep apnea in renal failure
Bibliographic record
Abstract
BACKGROUND: In patients with end-stage renal disease (ESRD), fluid overload may contribute to their high prevalence of obstructive sleep apnea (OSA) by increasing the amount of fluid displaced from the legs into the neck overnight, and possibly compressing the upper airway (UA). Indeed, in ESRD patients, the amount of overnight rostral fluid displacement from the legs is related to the frequency of apneas and hypopneas per hour of sleep (apnea-hypopnea index, AHI). We, therefore, hypothesized that in ESRD patients, the greater the UA-mucosal water content (UA-MWC) and internal jugular vein volume (IJVVol), the higher the AHI. METHODS: We studied 20 patients with ESRD on thrice weekly hemodialysis who had undergone diagnostic polysomnography (age 41.0 ± 12.3 years, with a body mass index (BMI) of 25.8 ± 6.3 kg/m(2) and an AHI of 20.2 ± 26.8). The leg fluid volume (LFV) was measured by bioelectric impedance. The IJVVol and MWC were measured by UA magnetic resonance imaging (MRI). RESULTS: The only significant independent correlates of the AHI were IJVVol (r = 0.801, P < 0.0001) and UA-MWC (r = 0.720, P = 0.0005) which together explained 72% of its variability. CONCLUSIONS: These data suggest that fluid overload via increased IJVVol, and UA-MWC, contributes to the pathogenesis of OSA in patients with ESRD. These findings help us to explain the high prevalence of OSA in ESRD patients, and attenuation of OSA in association with nocturnal dialysis. They also suggest the need for randomized trials to determine whether more aggressive fluid removal in ESRD patients will alleviate OSA.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".