Predicting Recurrence of Vasovagal Syncope: A Simple Risk Score for the Clinical Routine
Bibliographic record
Abstract
BACKGROUND: Predictors for recurrence of syncope are lacking in patients with vasovagal syncope. The aim of this study was to identify risk factors for recurrence of syncope and develop a simple prognostic risk score of clinical value. METHODS: Two hundred seventy-six patients with a history of vasovagal syncope were prospectively followed for 2 years. Diagnosis of vasovagal syncope was based on clinical history and negative standard work-up. Inclusion in the study was independent from the result of the head-up tilt test, which was performed in all cases. Risk factors for syncope recurrence were evaluated by the Cox proportional hazards regression model and implemented in a risk score, which was validated with the log-rank test and an internal cross-validation. RESULTS: The Cox-regression analysis identified the number of previous syncopal events, history of bronchial asthma, and female gender as predictors for syncope recurrence (all P < 0.05). In contrast, head-up tilt test response had no predictive value (P = 0.881). Developing a risk score, study patients were identified as having high (recurrence rate during 2 years of follow-up: 37.2%), intermediate (24.8%), and low (6.5%) risk for syncope recurrence (receiver operating characteristic [ROC] of score 0.83, P < 0.01; Log-rank test for event-free survival, P < 0.005). CONCLUSIONS: In patients with vasovagal syncope, risk of recurrence can be stratified and is predictable based on a simple risk score.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".