“Complex” Vasovagal Syncope: A Zebra Among Horses
Bibliographic record
Abstract
Background: Vasovagal syncope (VVS) occurs due to cerebral hypoperfusion from a fall in blood pressure, with accompanying bradycardia in most cases. Seizure and/or asystole may accompany VVS, though their prediction within the VVS cohort remains elusive. Objective: To further characterize VVS and to find predictive features of “complex” VVS (defined as VVS associated with seizures and/or asystole). Methods: We reviewed medical records of all patients who were referred for orthostatic intolerance and had a definite VVS during the head-up tilt table testing (HUTT). The following variables were recorded: cardiovascular indices during HUTT, autonomic testing results, and semiology of asystole and/or seizure when present. Simple frequency and correlation analysis were performed using the ANOVA. Results: A total of 78 independent VVS were recorded in 60 patients of which 24% were not preceded by presyncope. Vasodepressor (45%) and mixed (38%) VVS were the most prevalent types. Eighteen (23%) were complex VVS; five had an associated seizure (SySz), nine were accompanied by asystole (SyAs), and four had both (SySzAs). Males were significantly more likely to have complex VVS. Mean asystole duration was somewhat longer in the SyAsSz group. The severity of bradycardia significantly correlated with complex VVS and was a predictor of SySz. Autonomic abnormalities were frequent but did not distinguish the two VVS subgroups. Seizures had multiple distinguishing features from those typically associated with epileptic seizures. Conclusions: The underlying pathophysiologic mechanisms of complex VVS remain unclear, but the severity of cerebral hypoperfusion due to bradycardia likely plays a key role in seizure generation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".