Epidemiology of Acute Kidney Injury
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The worldwide incidence of acute kidney injury is poorly known because of underreporting, regional disparities, and differences in definition and case mix. New definitions call for revision of the problem with unified criteria. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This article reports on the research recommendations of an international multidisciplinary committee, assembled to define a research agenda on acute kidney injury epidemiology using a modified three-step Delphi process. RESULTS: Knowledge of incidence and risk factors is crucial because it drives local and international efforts on detection and treatment. Also, notable differences exist between developing and developed countries: Incidence seems higher in the former, but underreporting compounded by age and gender disparities makes available data unreliable. In developing countries, incidence varies seasonally; incidence peaks cause critical shortages in medical and nursing personnel. Finally, in developing countries, lack of systematic evaluation of the role of falciparum malaria, obstetric mechanisms, and hemolytic uremic syndrome on acute kidney injury hampers efforts to prevent acute kidney injury. CONCLUSIONS: The committee concluded that epidemiologic studies should include (1) prospective out- and inpatient studies that measure incidence of community and hospital acute kidney injury and post-acute kidney injury chronic kidney disease; (2) incidence measurements during seasonal peaks in developing and developed countries; and (3) whenever available, use of reliable existing administrative or institutional databases. Epidemiologic studies using standardized definitions in community and institutional settings in developing and underdeveloped countries are essential first steps to achieving early detection and intervention and improved patient outcomes.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".