Influence of body mass loss and myoglobinuria on the development of muscle fatigue after a marathon in a warm environment
Bibliographic record
Abstract
The aim of this study was to determine the changes in body mass and myoglobinuria concentration in recreational runners during a marathon in a warm environment, and the relation of these changes to muscle fatigue. We recruited 138 amateur runners (114 men and 24 women) for the study. Before the race, leg muscle power output was measured during a countermovement jump on a force platform, body weight was measured, and a urine sample was obtained. Within 3 min of race completion (28 °C; 46% relative humidity), the runners repeated the countermovement jump, body weight was measured again, and a second urine sample was obtained. Myoglobin concentration was determined in the urine samples. After the race, mean body mass reduction was 2.2% ± 1.2%. Fifty-five runners (40% of the total) reduced their body mass by less than 2%, and 10 runners (7.2%) reduced their body mass by more than 4%. Only 3 runners increased their body mass after the marathon. Mean leg muscle power reduction was 16% ± 10%. Twenty-four runners reduced their muscle power by over 30%. No myoglobin was detected in the prerace urine specimens, whereas postrace urinary myoglobin concentration increased to 3.5 ± 9.5 μg·mL(-1) (p < 0.05). Muscle power change after the marathon significantly correlated with postrace urine myoglobin concentration (r = -0.55; p < 0.001), but not with body mass change (r = -0.08; p = 0.35). After a marathon in a warm environment, interindividual variability in body mass change was high, but only 7% of the runners reduced their body mass by more than 4%. The correlation between myoglobinuria and muscle power change suggests that muscle fatigue is associated with muscle breakdown.
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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".