Clinical significance of heart rhythm variability in patients with gastroesophageal reflux disease
Bibliographic record
Abstract
Cardiac manifestations of gastroesophageal reflux disease (GERD) including retrosternal pain and cardiac rhythm disorders were often mentioned in early publications. However, classification of GERD adopted at the 2005 Montreal congress does not include such conditions. Non-coronarogenic pain in the thoracic cage is recognized to be a typical esophageal syndrome while the reflex spasm of coronary arteries and cardiac rhythm disorders associated with GERD should be regarded as manifestations of comorbidity of GERD and cardiovascular diseases. Arrhythmias occur in 30% of patients with GERD, but relevant therapeutic and preventive modalities are poorly developed. Nor are there reliable predictors of arrhythmias. An important role in their pathogenesis in patients with GERD is played bydisbalance of sympathetic and parasympathetic components of vegetative nervous system (VNS). One of the approaches to studying this issue is the analysis of heart rhythm variability (HRV). We consider basic principles of clinical interpretation of the results of HRV research that allow to evaluate the state of VNS and interaction of its components. Analysis of recent publications gives evidence that many HRV parameters depend not only on the balance between components of VNS as was believed by earlier authors (for the lack of their reciprocal relationship) but also on the activity of these components. Other modulating factors include the heart rate, respiratory pattern, intrathoracic pressure, and diastolic atrial extension. We report the results of HRV assessment in patients with GERD that illustrate heart rhythm rigidity and predominant disorders of activity of the parasympathetic component of VNS especially well apparent in patients with erosive reflux esophagitis. However, other authors demonstrate oppositely directed changes and the involvement of the sympathetic component. A possible cause of this discrepancy is factors other than VNS influencing HRV. It is concluded that analysis of HRV can be used to develop a method for the prevention of GERD-associated arrhythmia. The diagnostic value of such method is limited and requires new algorithms for data interpretation taking account of their multifactorial origin.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".