Acute kidney injury associated with endurance events—is it a cause for concern? A systematic review
Bibliographic record
Abstract
INTRODUCTION: A growing body of evidence suggests even small rises in serum creatinine (SCr) are of considerable clinical relevance. Given that participants in endurance events are exposed to potential (repeated) renal insults, a systematic review was undertaken to collate current evidence for acute kidney injury (AKI), complicating such events. METHODS: A systematic review of studies and case reports meeting inclusion criteria on Medline and EMBASE (inception to October 2015). Included: studies with markers of renal function before and after endurance or ultraendurance events; case reports of severe AKI. Two reviewers assessed risk of bias using the Newcastle-Ottawa scale. RESULTS: Eleven case report publications (n=27 individuals) of severe AKI, were retrieved, with risk factors including systemic illness or nephrotoxic medications usually identified. From 30 studies of endurance and ultraendurance events, mean rise in SCr was 29 (±12.3) µmol/L after marathon or ultramarathon (17 studies, n=568 participants) events. Where follow-up tests were conducted, SCr returned to baseline within 48 hours. Rises in biomarkers suggest potential parenchymal insult, rather than simply muscle breakdown. However, evidence of long-term deleterious effects is lacking. CONCLUSIONS: Raised levels of SCr are reported immediately after endurance events. It is not clear whether this is either clinically significant, or if repeated participation predisposes to long-term sequelae. The aetiology of severe exercise-associated AKI is usually multifactorial, with risk factors generally identified in the rare cases reported. On-site biochemistry, urine analysis and biomarkers of AKI may help identify collapsed runners who are at significant short-term risk and allow suitable follow-up.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.031 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".