Assessing Leg Blood Flow and Cardiac Output During Running Using Thermodilution
Bibliographic record
Abstract
ABSTRACT Cardiac output (Q̇C) and leg blood flow (Q̇LEG) can be measured simultaneously with high accuracy using transpulmonary and femoral vein thermodilution with a single‐bolus injection. The invasive measure has offered important insight into leg hemodynamics and blood flow distribution during exercise. Despite being the natural modality of exercise in humans, there has been no direct measure of Q̇LEG while running in humans. We sought to determine the feasibility of the thermodilution technique for measuring Q̇LEG and conductance during high‐intensity running, in an exploratory case study. A trained runner (30 years male) completed two maximal incremental tests on a cycle ergometer and motorized treadmill. Q̇LEG and Q̇C were determined using the single‐bolus thermodilution technique. Arterial and venous blood were sampled throughout exercise, with continuous monitoring of metabolism, intra‐arterial and venous pressure, and temperature. The participant reached a greater peak oxygen uptake (V̇O2peak) during running relative to cycling (74 vs. 68 mL/kg/min) with comparable Q̇LEG (19.0 vs. 19.5 L/min) and Q̇C (27.4 vs. 26.2 L/min). Leg vascular conductance was greater during high‐intensity running relative to cycling (82 vs. 70 mL/min/mmHg @ ~80% V̇O2peak). The “beat phenomenon” was apparent in femoral flow while running, producing large gradients in conductance (62–90 mL/min/mmHg @ 70% V̇O2peak). In summary, we present the first direct measure of Q̇LEG and conductance in a running human. Our findings corroborate several assumptions about Q̇LEG during running compared with cycling. Importantly, we demonstrate that using thermodilution in running exercise can be completed effectively and safely.
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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 | 0.001 |
| 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".