Machine learning for analysis of active stand responses in older adults with vasovagal syncope
Bibliographic record
Abstract
Abstract Objectives To assess 1) differences in the hemodynamic response to the active stand test in older adults with a clinical diagnosis of vasovagal syncope compared to age-matched controls 2) if the active stand test combined with machine learning approaches can be used to identify the presence of vasovagal syncope in older adults. Approach Adults aged 50 and over (Vasovagal Syncope N=46 Age=66.9±10.3; Control N=86 Age=65.3±9.5) completed an active stand test. Multiple features were extracted to characterize the hemodynamic responses to the active stand test and were compared between groups. Classification was performed using machine learning algorithms including linear discriminant analysis, quadratic discriminant analysis, support vector machine and an ensemble majority vote classifier. Main Results Subjects with vasovagal syncope demonstrated a higher resting (supine) heart rate (69.8±13.1 bpm vs 63.3±12.1 bpm; P=0.007), a smaller initial systolic blood pressure drop (−20.2±20.1% vs −27.3±17.5%; P=0.005), larger drops in stroke volume (−14.7±24.0% vs −2.7±23.3%; P=0.010) and cardiac output (−6.4±18.5% vs 5.8±22.3%;P<0.001) and a larger increase in total peripheral resistance (8.1±30.4% vs −6.03±22.8%; P=0.002) compared to controls. A majority vote classifier identified the presence of vasovagal syncope with 82.6% sensitivity, 76.8% specificity, and average accuracy of 78.9%. Significance Older adults with vasovagal syncope display a unique hemodynamic and autonomic response to active standing characterized by relative autonomic hypersensitivity and larger drops in cardiac output compared to age-matched controls. With suitable machine learning algorithms, the active stand test holds the potential to be used to screen older adults for reflex syncopes and hypotensive susceptibility potentially reducing test time, cost, and patient discomfort. More broadly this paper presents a machine learning framework to support use of the active stand test for classification of clinical outcomes of interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".