Evaluation of stroke volume estimation during orthostatic stress: the utility of Modelflow
Bibliographic record
Abstract
Advanced blood pressure monitoring devices contain algorithms that permit estimation of stroke volume (SV). Modelflow (Finapres Medical Systems) is one common method to non-invasively estimate beat-to-beat SV. However, Modelflow accuracy during profound reductions in SV is unclear. We aimed to compare SV estimation by Modelflow and echocardiography, at rest and during orthostatic challenge. We tested 13 individuals (age 24 ± 2 years; 7 female) using combined head-up tilt and graded lower body negative pressure, continued until presyncope. SV was derived by both Modelflow and echocardiography on multiple occasions while supine, during orthostatic stress, and at presyncope. SV index (SVI) was determined by normalising SV for body surface area. Bias and limits of agreement were determined using Bland-Altman analyses. Two one-sided tests (TOST) examined equivalency. Across all timepoints, Modelflow estimates of SV (73.2 ± 1.6 ml) were strongly correlated with echocardiography estimates (66.1 ± 1.3 ml) (r = 0.56, P < 0.001) with a bias of +7.1 ± 21.1 ml. Bias across all timepoints was further improved when SV was indexed (+3.6 ± 12.0 ml.m -2 ). Likewise, when assessing responses relative to baseline, Modelflow estimates of SV (-23.4 ± 1.4%) were strongly correlated with echocardiography estimates (-19.2 ± 1.3%) (r = 0.76, P < 0.001), with minimal bias (-4.2 ± 13.1%). TOST testing revealed equivalency to within 15% of the clinical standard for SV and SVI, both expressed as absolute values and relative to baseline. Modelflow can be used to track changes in SV during profound orthostatic stress, with accuracy enhanced with correction relative to baseline values or body size. These data support the use of Modelflow estimates of SV for autonomic function testing.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".