Ischemic Preconditioning: Modulating Pain Sensitivity and Exercise Performance
Bibliographic record
Abstract
Purpose The purpose of this study was to examine whether an individual’s IPC-mediated change in cold pain sensitivity is associated with the same individual’s IPC-mediated change in exercise performance. Methods Thirteen individuals (8 males; 5 females, 27 ± 7 years, 55 ± 5 ml.kgs–1.min–1) underwent two separate cold-water immersion tests: with preceding IPC treatment and without. In addition, each participant undertook two separate 5-km cycling time trials: with preceding IPC treatment and without. Pearson correlation coefficients were used to assess the relationship between an individual’s change in cold-water pain sensitivity following IPC with their change in 5-km time trial performance following IPC. Results During the cold-water immersion test, pain intensity increased over time (p < 0.001) but did not change with IPC (p = 0.96). However, IPC significantly reduced the total time spent under pain (−9 ± 7 s; p = 0.001) during the cold-water immersion test. No relationship was found between an individual’s change in time under pain (r = −0.2, p = 0.6) or pain intensity (r = −0.3, p = 0.3) following IPC and their change in performance following IPC. Conclusion These findings suggest that IPC can modulate sensitivity to a painful stimulus, but this altered sensitivity does not explain the ergogenic efficacy of IPC on 5-km cycling 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 | 0.000 |
| 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".