Minimal muscle damage after a marathon and no influence of beetroot juice on inflammation and recovery
Bibliographic record
Abstract
This study examined whether beetroot juice (BTJ) would attenuate inflammation and muscle damage following a marathon. Using a double blind, independent group design, 34 runners (each having completed ca. ∼16 previous marathons) consumed either BTJ or an isocaloric placebo (PLA) for 3 days following a marathon. Maximal isometric voluntary contractions (MIVC), countermovement jumps (CMJ), muscle soreness, serum cytokines, leucocytosis, creatine kinase (CK), high sensitivity C-reactive protein (hs-CRP), and aspartate aminotransferase (AST) were measured pre, post, and 2 days after the marathon. CMJ and MIVC were reduced after the marathon (P < 0.05), but no group differences were observed (P > 0.05). Muscle soreness was increased in the day after the marathon (BTJ; 45 ± 48 vs. PLA; 46 ± 39 mm) and had returned to baseline by day 2, irrespective of supplementation (P = 0.694). Cytokines (interleukin-6; IL-6, interleukin-8, tumour necrosis factor-α) were increased immediately post-marathon but apart from IL-6 had returned to baseline values by day 1 post. No interaction effects were evident for IL-6 (P = 0.213). Leucocytes increased 1.7-fold after the race and remained elevated 2 days post, irrespective of supplement (P < 0.0001). CK peaked at 1 day post marathon (BTJ: 965 ± 967, and PLA: 1141 ± 979 IU·L−1) and like AST and hs-CRP, was still elevated 2 days after the marathon (P < 0.05); however, no group differences were present for these variables. Beetroot juice did not attenuate inflammation or reduce muscle damage following a marathon, possibly because most of these indices were not markedly different from baseline values in the days after the marathon.
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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) | 0.001 | 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".