Acute kidney injury during an ultra-distance race
Bibliographic record
Abstract
PURPOSE: Previous studies have noted consequences of ultra-distance trail running on health, but few studies are available regarding the temporal variations of renal biomarker injury during the running. The aim of this study was to assess the of kidney function parameters temporal variation during and on short-term after an ultra-distance race. METHODS: We performed an observational study with 47 subjects participating in an ultra-distance race (80 km). Urine (47 subjects) and blood (21 subjects) samples were serially collected before (baseline-km 0), during (21 and 53 km), on arrival (80 km), and 9 days after the race (d9). RESULTS: Mean serum creatinine increased during the race from 90±14 μmol/L (km0) to 136±32 μmol/L (km 80-p<0.0001) corresponding to a 52% increase. Mean creatininuria progressively increased from 4.7±4.5 mmol/L (km 0) to 22.8±12.0 mmol/L (km 80) (p<0.0001). Both urinary biomarkers (Neutrophil Gelatinase Associated Lipocalin and Kidney Injury Molecule-1) of acute kidney injury (AKI) progressively increased during the race (p<0.05 vs baseline). However, after adjustment to urine dilution by urine creatinine, no significant changes remained (p>0.05). On day 9, no significant difference remains in blood and urine biomarkers compared to their respective baseline levels. CONCLUSIONS: During an ultra-distance race, despite an acute and transient increase in the serum creatinine levels, urinary biomarkers of AKI displayed only limited changes with a complete regression on day 9. These results suggest the absence of the short-term impact of an ultra-distance race kidney function.
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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 | 0.001 |
| 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".