A Machine Learning Framework to Detect Syncope using the Active Stand
Bibliographic record
Abstract
Abstract Background Vasovagal syncope (VVS) is the most common form of syncope, accounting for 50-60% of unexplained syncope. Currently diagnosis is achieved via clinical assessment combined with the Head-Up Tilt Test (HUT). Aim To examine the utility of the active stand test (AS) to identify those with a positive HUT or diagnosis of VVS. Design Retrospective study of hemodynamic responses to AS. Methods Continuous blood pressure responses to AS from 101 patients attending a Falls and Blackouts Unit were acquired, including: 37 controls (CON), 64 with a clinical diagnosis of VVS (VVS+) (34 tilt-positive (HUT+) and 30 tilt-negative (HUT-)) with a mean age of 25 ± 9 years. A total of 33 hemodynamic features were extracted with a subset of these entered into linear discriminant classifier. Classification accuracy was assessed using N-fold cross-validation. Results Results indicated that it was possible to classify the outcome of the HUT with sensitivity of 58.8%, specificity of 63.3% and an accuracy of 60.9%. Using a multivariate classifier it was possible to identify those with a positive diagnosis of VVS with a sensitivity of 84.3%, specificity of 72.9% and an accuracy of 80.2%. Conclusion This study highlights the existence of a unique AS hemodynamic response characterised by autonomic hypersensitivity exhibited by young patients prone to VVS which is detectable using a multi-parameter machine learning framework. With further verification, this approach may have applications in syncope and falls diagnosis, population studies and the tracking of treatment efficacy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".