New frontiers in obstructive sleep apnoea
Bibliographic record
Abstract
OSA (obstructive sleep apnoea), the most common respiratory disorder of sleep, is caused by the loss of upper airway dilating muscle activity during sleep superimposed on a narrow upper airway. This results in recurrent nocturnal asphyxia. Termination of these events usually requires arousal from sleep and results in sleep fragmentation and hypoxaemia, which leads to poor quality sleep, excessive daytime sleepiness, reduced quality of life and numerous other serious health consequences. Furthermore, patients with untreated sleep apnoea are at an increased risk of hypertension, stroke, heart failure and atrial fibrillation. Although there are many predisposing risk factors for OSA, including male gender, endocrine disorders, use of muscle relaxants, smoking, fluid retention and increased age, the strongest risk factor is obesity. The aim of the present review is to focus on three cutting-edge topics with respect to OSA. The section on animal models covers various strategies used to simulate the physiology or the effects of OSA in animals, and how these have helped to understand some of the underlying mechanisms of OSA. The section on diabetes discusses current evidence in both humans and animal models demonstrating that intermittent hypoxia and sleep fragmentation has a negative impact on glucose tolerance. Finally, the section on cardiovascular biomarkers reviews the evidence supporting the use of these biomarkers to both measure some of the negative consequences of OSA, as well as the potential benefits of OSA therapies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".