Optimal diagnostic thresholds for diagnosis of orthostatic hypotension with a ‘sit-to-stand test’
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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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".