The Effect of Phlebotomy on Physiological Responses during Submaximal, Prolonged Exercise
Bibliographic record
Abstract
INTRODUCTION: The influence of reduced blood volume on prolonged submaximal exercise is unclear. Using a sham-controlled design, we investigated the effect of acute phlebotomy on physiological responses to 60 min of submaximal exercise and its subsequent impact on severe-intensity exercise performance. METHODS: After baseline testing and a control trial, 17 moderately trained participants (5 female) underwent phlebotomy (PHLE) to withdraw 7% of total blood volume or a sham procedure (SHAM). Cardiorespiratory, metabolic, perceptual, and neuromuscular responses were assessed before, during, and after 60 min of submaximal exercise in the heavy domain (midway between the respiratory compensation point and the gas exchange threshold; 71% [6%] of V̇O 2max ) and in response to a subsequent severe-intensity time-to-task failure (TTF) trial. RESULTS: Phlebotomy significantly affected ventilation (V̇ E ; +6% [7%] vs control trial), ventilatory equivalent (+8% [8%]), heart rate (HR; +5% [4%]), O 2 pulse (-6% [5%]), and blood lactate ([La]; +25% [32%]) during submaximal exercise ( P < 0.05). Submaximal V̇O 2 , respiratory exchange ratio, perceived effort, and maximal voluntary contraction were unaffected by phlebotomy ( P > 0.05). Phlebotomy reduced TTF by 24% [23%] ( P = 0.018) without significantly reducing V̇O 2peak (-5.6% [7.5%], P = 0.09). Changes in V̇ E ( P = 0.004), HR ( P = 0.003), O 2 pulse ( P = 0.009), and [La] ( P < 0.001) between the control and experimental submaximal trials were correlated with changes in TTF (0.40 < R2 < 0.68). CONCLUSIONS: Circulating vascular volumes impact physiological responses to submaximal exercise and influence subsequent maximal exercise performance.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".