Remote Preconditioning Improves Maximal Performance in Highly Trained Athletes
Bibliographic record
Abstract
BACKGROUND: Remote ischemic preconditioning (RIPC) induced by transient limb ischemia releases a dialysable circulating protective factor that reduces ischemia-reperfusion injury. Exercise performance in highly trained athletes is limited by tissue hypoxemia and acidosis, which may therefore represent a type of ischemia-reperfusion stress modifiable by RIPC. METHODS AND RESULTS: National-level swimmers, 13-27 yr, were randomized to RIPC (four cycles of 5-min arm ischemia/5-min reperfusion) or a low-pressure control procedure, with crossover. In study 1, subjects (n=16) performed two incremental submaximal swimming tests with measurement of swimming velocity, blood lactate, and HR. For study 2, subjects (n=18) performed two maximal competitive swims (time trials). To examine possible mechanisms, blood samples taken before and after RIPC were dialysed and used to perfuse mouse hearts (n=10) in a Langendorff preparation. Infarct sizes were compared with dialysate obtained from nonathletic controls. RIPC released a protective factor into the bloodstream, which reduced infarct size in mice (P<0.05 for controls and swimmers). There was no statistically significant difference between the effect of RIPC and the low-pressure control protocol on submaximal exercise performance. However, RIPC was associated with a mean improvement of maximal swim time for 100 m of 0.7 s (P=0.04), an improvement in swim time relative to personal best time (-1.1%, P=0.02), and a significant improvement in average International Swimming Federation points (+22 points, P=0.01). CONCLUSIONS: RIPC improves maximal performance in highly trained swimmers. This simple technique may be applicable to other sports and, more importantly, to other clinical syndromes in which exercise tolerance is limited by tissue hypoxemia or ischemia.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".