Physiological and clinical comparison of active stand and head-up tilt tests in Postural Orthostatic Tachycardia Syndrome (POTS)
Bibliographic record
Abstract
Head-up tilt (HUT) and active stand tests (AST) are used in the diagnosis of Postural Orthostatic Tachycardia Syndrome (POTS), but their relative diagnostic accuracy is unclear. This necessitates a direct comparison under standardized conditions. We aimed to compare the hemodynamic responses and diagnostic accuracy of AST vs. HUT in POTS. To address this, patients with POTS ( n = 60) completed a 10-min AST followed by HUT on the same day. Beat-to-beat hemodynamics were recorded during 10-min supine baselines and each test. Delta values were calculated for each test (upright 1-min averages minus baseline average). Δ[heart rate] increased significantly over time (1_Min: 28 bpm to 10_Min: 40 bpm; P Time < 0.001), and was greater for HUT (33 bpm vs. 37 bpm; P ASTvHUT = 0.01), with significant Time x Condition interaction (38 bpm vs. 42 bpm at10min; P INT < 0.001). Δ[stroke volume] declined over time (1_Min: -18 ml to 10_Min: -32 ml); P Time < 0.001), with no significant test or interaction effects (P ASTvHUT = 0.36; P INT = 0.21). Δ[SBP] decreased (1_Min: −0.3 mmHg to 10_Min: −5.7 mmHg); P Time < 0.001) over time, with no test or interaction effects. Fewer patients met POTS heart rate criteria during the AST (AST: 74 % vs. HUT: 98 %; p < 0.001). Lowering the threshold to 27 bpm for AST narrowed the gap but was still significantly higher for HUT (AST: 83 % vs HUT: 98 %; p = 0.02). Orthostatic tachycardia differs between AST and HUT in patients with POTS. The proportion of patients with POTS meeting the heart rate diagnostic criteria differs significantly between AST and HUT, a discrepancy that can be mitigated by lowering the heart rate threshold for the AST.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".