Can lower limb ischemic preconditioning improve aerobic capacity in healthy adults? A systematic review and meta-analysis
Bibliographic record
Abstract
This systematic review and meta-analysis aimed to examine the effects of lower limb ischemic preconditioning (IPC/LL) on aerobic capacity in healthy adults. The search was conducted in five electronic databases. Two authors independently reviewed the search results, extracted the data, and assessed the risk of bias and certainty of evidence. Meta-analyses and subgroup analyses were performed to determine the overall effect size and the impact of potential moderators. Twenty publications consisting of 297 participants were included. The overall analysis showed that time to exhaustion was significantly improved after IPC/LL intervention compared with the control (Hedges' g = 0.40, 95% CI (0.16, 0.64), p < 0.01). In subgroup analysis, time to exhaustion was significantly improved only in single-pass intervention conditions, in untrained participants, and assessed by cycling exercise tests ( p < 0.05). However, no significant effect was observed on time trial performance (Hedges' g = −0.08, 95% CI (−0.33, 0.16), p = 0.50), peak oxygen uptake (Hedges' g = 0.02, 95% CI (−0.17, 0.21), p = 0.85), and blood lactate (Hedges' g = 0.09, 95% CI (−0.06, 0.23), p = 0.26) in healthy adults after IPC/LL intervention. This systematic review and meta-analysis provides moderate evidence that IPC/LL does not improve the aerobic capacity of healthy adults but contributes to an enhancement in time to exhaustion during aerobic exercise.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.029 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".