Exercise proteinuria and hematuria: current knowledge and future directions.
Bibliographic record
Abstract
INTRODUCTION: Transient proteinuria and hematuria are apparently benign sequelae of intensive physical activity. However, there is a need to establish underlying causes and reasons for progression to chronic renal damage, as well as effects of training in healthy individuals and in those with microalbuminuria. EVIDENCE ACQUISITION: The Ovid/Health Star database was searched from 1994 to November 2014. Terms for the kidneys (adverse effects, blood supply, epidemiology, injuries, pathology, physiology and secretion) and proteinuria (classification, complications, epidemiology, etiology, mortality, physiopathology, prevention and control) with terms related to physical activity (physical activity/motor activity, exercise/exercise therapy, fitness/physical fitness, physical education/physical education and training, and rehabilitation). EVIDENCE SYNTHESIS: Review of 519 abstracts yielded 194 relevant hits, supplemented by 70 items from other sources. This material related to both healthy adults (125 items) and renal disease (139 items). The prevalence (18-100%) and duration (1-6 days) of exercise proteinuria varied widely, with risks affected by exercise intensity, posture, age, heat load, altitude and disease. Moderate training reduced exercise proteinuria in healthy individuals and in chronic renal disease. Factors contributing to exercise proteinuria may include vascular changes, hypoxia, lactate accumulation, oxidant stress, hormonal changes and sepsis. Exercise hematuria is frequent; some potential causes are similar to those for proteinuria, but foot-strike and bladder trauma are probably more important. Progression to permanent renal damage is rare. CONCLUSIONS: Exercise proteinuria and hematuria are generally transient. However, there remains a need to clarify causation and factors leading to permanent renal damage.
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 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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".