Advances in Treating Vasovagal Neurocardiogenic Syndrome: A Comprehensive Review of Medical Interventions
Bibliographic record
Abstract
Neurocardiogenic vasovagal syndrome (VVS) is a common clinical condition that results in a transient loss of consciousness and inability to maintain posture, with a rapid and spontaneous recovery. Considering the technological advances regarding the effectiveness of different treatments for VVS, this article aims to review the treatment options available in the medical literature to better understand the treatment options and their potential benefits. This study is a literature review of the medical literature focused on publications from 2005 to 2022 related to the therapeutic management of VVS. Digital databases such as PubMed and SciELO were searched using the descriptors “vasovagal syncope”, “neurocardiogenic syncope” and “treatment of vasovagal syncope” to identify relevant studies. Orthostatic training (or tilt training) is a non-pharmacological approach that involves postural training performed through multiple sessions of orthostasis. Tilt training (TTr) proved to be an effective therapeutic method with long-term benefits in refractory patients. Pharmacological treatment should be considered in a case-by-case scenario. Cardioneuroablation is a procedure that has been shown to eliminate or significantly reduce the vagal response, leading to symptom relief in up to 75% of patients. Results showed that implementing a definitive pacemaker reduced symptoms in at least one-third of patients. In summary, treatment strategies for VVS are evolving with advances in medical research, allowing for a thorough analysis of each modality to determine its suitability. It is crucial to emphasize that the selection of treatment options should be evaluated by a specialist individually to ensure effective management of the patient’s clinical manifestations. Thus, available interventions have the potential to improve patient’s quality of life significantly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".