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 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".