Increased risk of acute kidney injury in the first part of an ultra‐trail—Implications for abandonment
Bibliographic record
Abstract
Acute kidneys injuries (AKIs) have been described in marathon and trail running. The currently available data allows assessment of before/after comparisons but does not allow an analysis of what happens during the race. A multidisciplinary assessment protocol was performed during the first trail of Clécy (Normandy France) in November 2021. This allowed an initial assay to be carried out, then at the end of each of the 6 loops of 26 km, and finally after 24 h of recovery. The race extends over 156 km in hilly terrain and 6000 m of elevation gain (D+). The level of impairment according to the RIFLE classification was defined for each runner at each assay. Fifty-five runners were at the start, and the per protocol analysis involved 36 runners (27 men and 9 women, 26 finishers). Fifteen (41.7%) of the riders presented at least one result corresponding to a "RIFLE risk" level. After 24 h of rest, only one runner still had a "RIFLE Risk". The distance around the marathon seems to be the moment of greatest risk. For the first time, we find an association between this renal risk and the probability of abandonment. Many runners are vulnerable to kidney damage during long-duration exercise, which is why it's important to limit risk situations, such as the use of potentially toxic drugs or hydration disorders. The consumption of NSAIDs (nonsteroidal anti-inflammatory drugs) before or during an ultra-distance race should therefore be prohibited. Attention should be paid to hydration disorders.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".