The Link Between Remote Ischemic Preconditioning, Performance And Oxygen Uptake Kinetics
Bibliographic record
Abstract
PURPOSE: One of the first assumption was that remote ischemic preconditioning (RIPC) acts on different mechanisms such as the stimulation of adenosine and the opening of mitochondrial ATP-sensitive potassium channels and therefore could result in a better oxygen distribution in sports performance. Even if RIPC did result in better time-trial performance, subsequent studies reported that RIPC does not affect maximal O2 consumption, hemodynamics and the anaerobic metabolism. Our hypothesis is that the better performance is due to a smaller oxygen deficit. Therefore, this study investigates if RIPC could reduce the oxygen deficit on the oxygen uptake kinetics. METHODS: Fifteen healthy participants were randomly assigned in a crossover design to an RIPC intervention and a control intervention (CON) before performing two consecutive bouts of 8-minute exercise at 75% and 115% of gas exchange threshold (GET) separated by a 20-minute passive rest. RESULTS: The primary time constant (τ1) of the fast component at moderate and heavy intensity is significantly different between RIPC and control intervention. At 75% of GET the mean SD of τ1 for RIPC is 27.2 4.6 seconds vs. 33.7 6.2 seconds for CON (p < 0.001). At 115% of GET, τ1 for RIPC is 29.9 4.9 seconds vs. 33.5 4.1 seconds for CON (p < 0.01); the amplitude of the fast component for RIPC is 2.09 0.42 L O2/min and 2.24 0.42 L O2/min (p = 0.031). CONCLUSIONS: RIPC speeds the O2 kinetics at moderate and high intensity level of exercise.
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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 | 0.003 |
| 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".