The Effects Of Remote Ischemic Preconditioning On Anaerobic Cycling Tests.
Bibliographic record
Abstract
BACKGROUND: Remote ischemic preconditioning (RIPC) provides a substantial benefit for heart protection during surgery. Recent literature on RIPC reveals the potential to benefit the enhancement of sports performance as well. The aim of this study is to investigate the effect of RIPC on anaerobic performance. METHODS: :Seventeen healthy participants who practice regular physical activity participated in the project (nine women and eight men, mean age 28 years + / - 8 years). The participants were randomly assigned to an RIPC intervention (four 5-minute cycles of ischemia reperfusion, followed by five minutes using a pressure cuff) or a SHAM intervention in a cross-over design. After the intervention, the participants were tested for alactic anaerobic performance (six seconds of effort) followed by a Wingate test (lactic system) on an electromagnetic cycle ergometer (Excalibur, Lode BV, Groningen, Netherlands). The following parameters were evaluated: average power, peak power, the scale of perceived exertion, fatigue index (W/s), peak power (W), time to reach peak power (s), minimum power (W), the average power-to-weight ratio (W/kg) and the maximum power-to-weight ratio (W/kg). RESULTS: :The peak power for the Wingate test is 794 W for RIPC and 777 W for the control group (p = 0.208). The average power is 529 W (RIPC) versus 520 W for controls (p = 0.079). Perceived effort for RIPC is 9/10 on the Borg scale versus 10/10 for the control group (p = 0.123). CONCLUSION: Remote ischemic preconditioning does not offer any significant benefits for anaerobic 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 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".