Modelflow estimates of cardiac output compared with Doppler ultrasound during acute changes in vascular resistance in women
Bibliographic record
Abstract
We compared Modelflow (MF) estimates of cardiac stroke volume (SV) from the finger pressure-pulse waveform (Finometer) with pulsed Doppler ultrasound (DU) of the ascending aorta during acute changes in total peripheral resistance (TPR) in the supine and head-up-tilt (HUT) postures. Twenty-four women were tested during intravenous infusion of 0.005 or 0.01 microg kg(-1) min(-1) isoprenaline, 10 or 50 ng kg(-1) min(-1) noradrenaline and 0.3 mg sublingual nitroglycerine. Responses to static hand-grip exercise (SHG), graded lower body negative pressure (LBNP, from 20 to 45 mmHg) and 45 deg HUT were evaluated on separate days. Bland-Altman analysis indicated that SV(MF) yielded lower estimates than SV(DU) during infusion of 0.01 microg kg(-1) min(-1) isoprenaline (SV(MF) 92.7 +/- 15.5 versus SV(DU) 104.3 +/- 22.9 ml, P = 0.03) and SHG (SV(MF) 78.8 +/- 12.0 versus SV(DU) 106.1 +/- 28.5 ml, P < 0.01), while larger estimates were recorded with SV(MF) during 45 mmHg LBNP (SV(MF) 52.6 +/- 10.7 versus SV(DU) 46.2 +/- 14.5 ml, P = 0.04) and HUT (SV(MF) 59.3 +/- 13.6 versus SV(DU) 45.2 +/- 11.3 ml, P < 0.01). Linear regression analysis revealed a relationship (r(2) = 0.41, P < 0.01) between the change in TPR from baseline and the between-methods discrepancy in SV measurements. This relationship held up under all of the experimental protocols (regression for fixed effects, P = 0.46). These results revealed a discrepancy in MF estimates of SV, in comparison with those measured by DU, during acute changes in TPR.
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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 | 0.000 | 0.002 |
| 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".