Sudden Emptying Of Forearm Venous Volume
Bibliographic record
Abstract
Immediate emptying of veins with muscle contraction may contribute to the rapid elevation of muscle blood flow observed at exercise onset. The mechanism(s) involved may be either a mechanical (muscle pump) or vasodilatory (venous emptying mediated dilation) or both. PURPOSE We tested the hypothesis that sudden emptying of limb venous volume, as would occur with muscle contraction, can rapidly increase limb blood flow and that this increase is proportional to the initial venous congestion pressure. METHODS 6 healthy young female subjects lay supine with their left arm extended at heart level. Subjects were instrumented for brachial artery forearm blood flow (FBF; Doppler ultrasound), arterial blood pressure (ABP; arterial tonometry) and heart rate (HR; ECG). A congestion cuff (CC) was placed around the upper arm, and an expulsion cuff (EC) encompassed the entire forearm. The CC was inflated to a given congestion pressure for 6 minutes, at which time it was deflated simultaneous with a 1 s EC inflation/deflation at 100 mmHg to simulate forearm contraction-induced emptying of veins. Congestion pressures examined were 20,40,60,80 and 100 mmHg. RESULTS Neither HR nor ABP changed during or following cuff inflations. The %increase in FBF immediately (first cardiac cycle) following venous expulsion was linearly related to initial venous congestion pressure (%increase in FBF =6.4(venous congestion pressure)-55.7, r2=0.99). In all but the 20 mmHg CC trials FBF increased further in subsequent cardiac cycles, peaking by the 4th cardiac cycle. The peak %increase in FBF was also linearly related to initial venous congestion pressure (%increase in FBF =13.6(venous congestion pressure)-207.6, r2=0.99). CONCLUSION These data support venous emptying mediated mechanisms for elevating limb blood flow that are dependent on the initial venous distension pressure. The immediate response may represent a muscle pump effect, while the peak response must be due to vasodilatory mechanism(s). Supported by NSERC; Canada Foundation for Innovation; Ontario Innovation Trust
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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".