An examination of individual responses to ischemic preconditioning and the effect of repeated ischemic preconditioning on cycling performance
Bibliographic record
Abstract
Abstract Purpose: To use repeated control trials to measure within‐subject variability and assess the existence of responders to ischemic preconditioning (IPC). Secondly, to determine whether repeated IPC can evoke a dosed ergogenic response. Methods: Twelve aerobically fit individuals each completed three control and three IPC 5‐km cycling time trials. IPC trials included: (i) IPC 15‐min preceding the trial (traditional IPC), (ii) IPC 24‐h and 15‐min preceding (IPC × 2), (iii) IPC 48‐h, 24‐h, and 15‐min preceding (IPC × 3). IPC consisted of 3 × 5‐min cycles of occlusion and reperfusion at the upper thighs. To assess the existence of a true response to IPC, individual performance following traditional IPC was compared to each individual's own 5‐km TT coefficient of variation. In individuals who responded to IPC, all three IPC conditions were compared to the mean of the three control trials (CONavg) to determine whether repeated IPC can evoke a dosed ergogenic response. Results: 9 of 12 (75%) participants improved 5‐km time (−1.8 ± 1.7%) following traditional IPC, however, only 7 of 12 (58%) improved greater than their own variability between repeated controls (true responders). In true responders only, we observed a significant mean improvement in 5‐km TT completion following traditional IPC (478 ± 50 s), IPC × 2 (481 ± 51 s), and IPC × 3 (480.5 ± 49 s) compared to mean CONavg (488 ± 51s; p < 0.006), with no differences between various IPC trials (p > 0.05). Conclusion: A majority of participants responded to IPC, providing support for a meaningful IPC‐mediated performance benefit. However, repeated bouts of IPC on consecutive days do not enhance the ergogenic effect of a single bout of IPC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.001 | 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".