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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".