The impact of a single bout of intermittent pneumatic compression on performance, inflammatory markers, and myoglobin in football athletes
Bibliographic record
Abstract
Objective: Intermittent Pneumatic Compression (IPC) use as a tool for recovery after exercise has recently become widespread among athletes. While there is anecdotal support for IPC, little research has been done to show its effectiveness in recovery. This study examined the impact of IPC use for recovery on performance, markers of inflammation, and a marker of muscle damage. Design: Eight university football athletes were recruited and subjected to IPC or passive recovery conditions in a randomized crossover manner following off-season training. Methods: Countermovement jump and 10 m sprint were evaluated before training, at 3 and 24 hours following training. Self reported soreness, blood markers of inflammation (interleukin-6, interleukin-10, and monocyte chemoattractant protein-1) and muscle damage (myoglobin) were measured before training, post-training, immediately after the recovery interventions, and at 3 and 24 hours post-training. Results: Significant time effects were observed in monocyte chemoattractant protein-1 and myoglobin suggesting an inflammatory response and muscle damage. No group differences were observed between recovery interventions for all measures. Conclusion: The results suggest that the IPC protocol used was not effective for the specific exercise paradigm and for the parameters measured in this population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".