70 * COMBINING THE ACTIVE STAND TEST AND PATTERN RECOGNITION ENABLES VASOVAGAL SYNCOPE PREDICTION
Bibliographic record
Abstract
Introduction: Vasovagal syncope (VVS) is the most common form of syncope, accounting for 50-60% of unexplained syncope. The gold standard of diagnosis is the Head-Up Tilt (HUT) test, a resource intensive procedure. This study aims to assess the accuracy of applying a pattern recognition methodology to predicting HUT outcome based on AS responses. Methods: Continuous blood pressure records obtained during an AS were acquired from patients attending a Falls and Blackout Unit. Patients were categorized into 3 groups based on their clinical history and HUT response: controls (CON), tilt-positive (HUT+) and tilt-negative (HUT-). Data from subjects diagnosed with VVS i.e. HUT+ and HUT- were combined to form a vasovagal positive (VVS+) group. Hemodynamic features (n = 33) were extracted from AS responses and entered into a linear discriminant classifier. Classifier training and accuracy was achieved using an N-fold cross validation procedure. Results: N = 101 patients were recruited (25 ± 9 years; 66% male) of whom 37 were CON, 30 were HUT- and 34 were HUT+. Maximum prediction accuracy of HUT response was 60.9% (range: 58.2-60.9%), with a sensitivity of 58.8% and specificity of 63.3%. A multivariate classifier enabled us to distinguish between VVS+ and CON with a maximum accuracy of 80.2% (range: 76.4-80.2%), sensitivity of 84.3% and specificity of 72.9%. Conclusion: This study highlights the existence of an alternative hemodynamic response to an AS test exhibited by young patients prone to VVS. Based on these responses, it was possible to identify the presence of VVS in younger people, using multi-physiological-parameter classification approaches from active stand, with an accuracy of 80% - a potential improvement on the HUT classification accuracy reported in literature (26% to 87%). With prospective verification, this approach may form the basis of a novel tool for syncope 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".