Limb Specific Vascular Adjustments at Exercise Onset
Bibliographic record
Abstract
Objective To compare the rate of vascular adjustment at the onset of arm vs. leg exercise using the same exercise modality and hydrostatic conditions. Methods 10 healthy recreationally active males (21.9±1.9 yrs) completed step increase dynamic exercise protocols (1s contract/2s relax) at 40%, 80% and 100% of peak vascular conductance response. Forearm handgrip and knee extension was performed in supine position with the exercising muscles at heart level. Continuous measures of brachial (forearm) and femoral (leg) artery blood flow (FBF, LBF; Doppler and Echo ultrasound) and arterial blood pressure (MAP; Finometer) were used to calculate forearm (FVC) and leg (LVC) vascular conductance. Results For all exercise intensities, LVC was (p<0.001) but FVC was not (P=0.705) characterized by a subsequent vasoconstriction at ~10 s (Phase IB) following the rapid initial vasodilation (Phase IA). Phase IA LVC amplitude was greater than steady state in the leg at 40% and no different at 80% (P<0.05), but for FVC it was lower than steady state at all exercise intensities (P<0.05). Phase IA had a greater contribution to the total response amplitude in the leg vs. forearm at all exercise intensities (P<0.05) representing an overshoot compared to the Phase II plateau at 40% and 80% (P<0.05). There was a main effect of limb for the Phase II time constant (leg faster than forearm, P<0.05). Conclusion When contraction/relaxation modality and hydrostatic environment are controlled for, rapid vasodilatory mechanisms are more sensitive to the onset of exercise in lower vs. upper limbs and can overshoot steady state. Transient vasoconstriction between the initial rapid and secondary slower adjustment is specific to the lower limb. Funding: NSERC 250367‐06.
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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) | 0.002 | 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".