Physiological and Biochemical Evaluation of Different Types of Recovery in National Level Paralympic Powerlifting
Bibliographic record
Abstract
BACKGROUND: Recovery from training is vital as it ensures training and performance to continue at high intensities and longer durations to stimulate the body and cause further adaptations. OBJECTIVE: To evaluate different methods of post-workout recovery in Paralympic powerlifting athletes. METHODS: Twelve male athletes participated (25.4 ± 3.3 years; 70.3 ± 12.1 kg). The presence of muscle edema, pain threshold, plasma cytokines, and performance measurement were evaluated five times. The recovery methods used in this study were passive recovery (PR), dry needling (DN), and cold-water immersion (CWI). RESULTS: The data analysis showed that the maximal force decreased compared to the pretest value at 15 min and 2 h. The results also revealed that CWI and DN increased Interleukin 2 (IL-2) levels from 24 to 48 h more than that from 2 h to 24 h. After DN, muscle thickness did not increase significantly in any of the muscles, and after 2 h, muscle thickness decreased significantly again in the major pectoralis muscle. After CWI, pain pressure stabilized after 15 min and increased significantly again after 2 h for acromial pectoralis. CONCLUSION: The strength training sessions generate several changes in metabolism and different recovery methods contribute differently to maintain homeostasis in Paralympic powerlifting athletes.
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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) | 0.002 | 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".