Bibliographic record
Abstract
Dear Editor-in-Chief, We thank da Mota and Marocolo (1) for their interest in our article (2) examining the impact of ischemic preconditioning (IPC) on performance at altitude, and we wish to address the elements raised in their commentary. As the authors noted, our study was the first to investigate changes in central (O2 delivery) and peripheral (O2 extraction) physiological responses and time-trial performance at altitudes commonly visited by athletes to train/compete. Results demonstrate that IPC may be more relevant to conditions in which arterial hypoxemia becomes a challenge since IPC induced clear beneficial effects on performance at 2400 m (SaO2 ~83%) but not at 1200 m (SaO2 ~90%) (2). The scarce studies performed on this topic had used altitudes above 3500 m and/or open-loop tests that do not represent a true athletic performance since participants could not pace themselves appropriately. Therefore, our results are particularly relevant to the sport community and serve as proof-of-concept for a new strategy with potential to mitigate the deleterious effects of hypoxia. We acknowledge, however, that our experimental design (i.e., IPC performed in normoxia, and the short timeframe between the IPC maneuver and the performance test) is not directly transferable to the field because most athletes would either travel to altitude from sea level within a few hours or be at altitude well in advance of a competition (3). In this perspective, it is important to mention that the ergogenic impact of IPC has been shown to last at least 8 h in sprint swimming (4). Whether this time course of decay is similar with endurance performance is unknown. Furthermore, the second window of effectiveness on tissue protection has still not been robustly examined from a sport performance standpoint and represents an opportunity for athletes whose logistical and financial constraints prevent them from proper acclimatization. In the situation where IPC would be performed at altitude to enhance acute competitive performance or high-intensity training quality, we feel that additional risks, if any, are likely minimal. Indeed, to our best knowledge, no study has reported deleterious effects of IPC in a hypoxic environment. For example, the application of IPC to both legs at 22 5 mm Hg after an exposure of 8 to 12 d at 3800-m terrestrial altitude did not change peripheral oxygen saturation, heart rate, blood pressure, pulmonary artery pressure, flow-mediated dilation and middle cerebral artery blood velocity at rest up to 48 h post-IPC compared with placebo (5). Moreover, at the muscle level, the effects of hypoxia at rest on tissue function and viability are likely to be modest at most (6). For example, resting tissue saturation index is not significantly altered with inspired O2 fractions down to 0.12 (7,8). This would suggest the combination of hypoxic and IPC stress might not be significantly greater than IPC alone. A better question might rather be, is IPC still beneficial at altitude after a well-conducted chronic acclimatization? To conclude, we thank da Mota and Marocolo (1) for giving their thoughts on our article. This discussion should stimulate future research on this exciting topic of athletic performance enhancement. Pénélope Paradis-Deschênes Denis R. Joanisse François Billaut Department of Kinesiology Laval University and Quebec Heart and Lung Institute Quebec QC, CANADA
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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 | 0.003 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.123 | 0.065 |
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".