Optimal diagnostic thresholds for diagnosis of orthostatic hypotension with a ‘sit-to-stand test’
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: This study aimed to identify optimal blood pressure cut-offs to diagnose orthostatic hypotension during a sit-to-stand manoeuvre. METHODS: This was a cross-sectional study of patients and healthy controls from the Vanderbilt Autonomic Dysfunction Center. Blood pressure was measured while supine, seated and standing. Blood pressure changes were calculated from supine-to-standing and seated-to-standing. Orthostatic hypotension was diagnosed on the basis of a supine-to-standing SBP drop at least 20 mmHg or a DBP drop at least 10 mmHg. Receiver operator characteristic (ROC) curves identified optimal sit-to-stand cut-offs. RESULTS: Amongst the 831 individuals, more had systolic orthostatic hypotension [n = 354 (43%)] than diastolic orthostatic hypotension [n = 305 (37%)] during lying-to-standing. The ROC curves had good characteristics [SBP area under curve = 0.916 (95% confidence interval: 0.896-0.936), P < 0.001; DBP area under curve = 0.930 (95% confidence interval: 0.909-0.950), P < 0.001]. A sit-to stand SBP drop at least 15 mmHg had optimal test characteristics (sensitivity = 80.2%; specificity = 88.9%; positive predictive value = 84.2%; negative predictive value = 85.8%), as did a DBP drop at least 7 mmHg (sensitivity = 87.2%; specificity = 87.2%; positive predictive value = 80.1%; negative predictive value = 92.0%). CONCLUSIONS: A sit-to-stand manoeuvre with lower diagnostic cut-offs for orthostatic hypotension provides a simple screening test for orthostatic hypotension in situations wherein a supine-to-standing manoeuvre cannot be easily performed. Our analysis suggests that a SBP drop at least 15 mmHg or a DBP drop at least 7 mmHg best optimizes sensitivity and specificity of this sit-to-stand test.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it