Sleep Disordered Breathing And Ventricular Arrhythmias: Mechanisms and Implications
Bibliographic record
Abstract
Sleep-Disordered breathing (SDB) describes a group of disorders characterized by abnormalities of respiratory pattern (pauses in breathing) or the quantity of ventilation during sleep. Sleep disordered breathing, including the sleep apnea syndrome, is demanding the attention of clinicians and researchers due to its high prevalence, detrimental impact on quality of life and association with a myriad of morbidities in multiple body systems. SDB has been associated with an increased risk for cardiovascular diseases. Life-threatening cardiac arrhythmias are of utmost importance because of their clinical implications. Atrial fibrillation (AF) has received most of the attention and its associations with SDB are well characterized. 1,2 This is in part, due to an increased risk of stroke in patients with SDB. Increased AF prevalence among patients with SDB is considered the main physiopathological explanation for this morbid association. Recent evidence also aids to establish the association between SDB and ventricular arrhythmias. Previous reports have suggested a high prevalence of SDB in populations at risk for ventricular arrhythmias and sudden cardiac death (SCD). The prevalence of SDB has been shown to be 50% or higher in patients with symptomatic heart failure and depressed left ventricular ejection fraction (LVEF), asymptomatic systolic dysfunction, and diastolic dysfunction. 3-5 These clinical conditions correspond to the populations of patients currently receiving implantable cardioverter defibrillators (ICDs) for the prevention of SCD due to ventricular arrhythmias, 6 specifically patients having survived ventricular tachycardia or fibrillation (secondary prevention) and patients with prior myocardial infarction (MI) and advanced left ventricular dysfunction (primary prevention). 7-9 The pathophysiological mechanisms that link SDB to ventricular arrhythmia remain undetermined. The following speculative mechanisms
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".