Effects of ischemic preconditioning on short-duration cycling performance
Bibliographic record
Abstract
It has been demonstrated that ischemic preconditioning (IPC) improves endurance performance. However, the potential benefits during anaerobic events and the mechanism(s) underlying these benefits remain unclear. Fifteen recreational cyclists were assessed to evaluate the effects of IPC of the upper thighs on anaerobic performance, skeletal muscle activation, and metabolic responses during a 60-s sprint performance. After an incremental test and a familiarization visit, subjects were randomly submitted in visits 3 and 4 to a performance protocol preceded by intermittent bilateral cuff inflation (4 × (5 min of blood flow restriction + 5 min reperfusion)) at either 220 mm Hg (IPC) or 20 mm Hg (control). To increase data reliability, each intervention was replicated, which was also in a random manner. In addition to the mean power output, the pulmonary oxygen uptake, blood lactate kinetics, and quadriceps electromyograms (EMGs) were analyzed during performance and throughout 45 min of passive recovery. After IPC, performance was improved by 2.1% compared with control (95% confidence intervals of 0.8% to 3.3%, P = 0.001), followed by increases in (i) the accumulated oxygen deficit, (ii) the amplitude of blood lactate kinetics, (iii) the total amount of oxygen consumed during recovery, and (iv) the overall EMG amplitude (P < 0.05). In addition, the ratio between EMG and power output was higher during the final third of performance after IPC (P < 0.05). These results suggest an increased skeletal muscle activation and a higher anaerobic contribution as the ultimate responses of IPC on short-term 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".