WISE‐2005: Drug‐induced changes in compliance affect model flow calculation of stroke volume
Bibliographic record
Abstract
Model flow (MF) estimates of cardiac stroke volume (SV) from the finger pressure wave form (Finometer) have been compared to SV estimates from pulsed Doppler ultrasound (DU) of the ascending aorta. Data were collected at rest and during the administration of 0.005 and 0.01 ug/kg/min isoproterenol (Iso), 10 and 50 ng/kg/min norepinephrine (NE) by intravenous infusion, and 0.3 mg nitroglycerin (NG) by sublingual spray. Data are from 12 women participating in the WISE bed rest study in the pre-bed rest condition. There was no significant difference between SV recorded with DU (68.3±10.2 ml, mean±SD) and MF (70.7±11.5 ml) at baseline (p=0.22). However, estimates of SV differed (p<0.01) between DU and MF during 0.01 ug/kg/min Iso (DU +57±20%, MF +21±21%), 50 ng/kg/min NE (DU −14±12%, MF +7±7%), and NG (DU +7±12 %, MF −8±11%). The model flow method uses assumed values for aortic compliance and peripheral vascular resistance. In this investigation total peripheral resistance (TPR) was significantly increased from baseline (1.3 PRU) during 50 ng/kg/min NE (1.7 PRU, p<0.01) and reduced during 0.01 ug/kg/min Iso (0.6 PRU, p<0.01) and 0.3 mg NG (1.1 PRU, p=0.04). Hence, with reduced TPR MF underestimated SV while an increase in TPR resulted in an overestimation. We have shown that SV values determined by MF should be interpreted with caution when measured during drug administration. Supported by Canadian Space Agency, NASA, ESA, CNES
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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 | 0.002 |
| 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) | 0.002 | 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".