Evaluation of Ibuprofen Use on the Immune System Indicators and Force in Disabled Paralympic Powerlifters of Different Sport Levels
Bibliographic record
Abstract
Background: Paralympic powerlifting (PP) training is typically intense and causes fatigue and alterations in the immune system. Objective: To analyze whether IBU would affect performance and the immune system after training in PP. Methodology: 10 athletes at the national level (NL) and 10 at the regional level (RL) participated in the study, where force and blood indicators were evaluated after training. The study took place over three weeks: (1) familiarization and (2 and 3) comparison between recovery methods, with ibuprofen or placebo (IBU vs. PLA), 800 mg. In the evaluation of the force, the peak torque (PT), fatigue index (FI), and blood immune system biomarkers were analyzed. The training consisted of five sets of five repetitions with 80% of one maximum repetition (5 × 5, 80% 1RM) on the bench press. Results: The PT at the national level using IBU was higher than with PLA (p = 0.007, η2p = 0.347), and the FI in the NL was lower with IBU than with PLA (p = 0.002, η2p = 0.635), and when comparing the use of IBU, the NL showed less fatigue than the regional level (p = 0.004, η2p = 0.414). Leukocytes, with the use of IBU in the NL group, were greater than in the RL (p = 0.001, η2p = 0.329). Neutrophils, in the NL with IBU, were greater than in the RL with IBU and PLA (p = 0.025, η2p = 0.444). Lymphocytes, in NL with IBU were lower than in RL with IBU and PLA (p = 0.001, η2p = 0.491). Monocytes, in the NL with IBU and PLA, were lower than in the RL with IBU (p = 0.049, η2p = 0.344). For hemoglobin, hematocrit, and erythrocyte, the NL with IBU and PLA were higher than the RL with IBU and PLA (p < 0.05). Ammonia, with the use of IBU in the NL, obtained values higher than in the RL (p = 0.007), and with the use of PLA, the NL was higher than the RL (p = 0.038, η2p = 0.570). Conclusion: The training level tends to influence the immune system and, combined with the use of the IBU, it tends to improve recovery and the immune system.
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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 | 0.001 |
| 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".