A Framework and Key Research Questions in AKI Diagnosis and Staging in Different Environments
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Acute Kidney Injury (AKI) is common worldwide, and associated with significant morbidity, mortality, and resource utilization. The RIFLE system of staging AKI correlates with survival in AKI in several settings. A similar AKI definition and staging system that also incorporates lesser degrees of serum creatinine elevation was proposed at the inaugural Acute Kidney Injury Network (AKIN) meeting in 2005. At the Second AKIN meeting in Vancouver, Canada in September 2006, our group developed a research agenda that would test the utility of these diagnostic and staging criteria to predict patient outcomes in a variety of clinical settings and patient groups. DESIGN, SETTING, PARTICIPANTS & MEASUREMENTS: Three-day, international, consensus conference. A multidisciplinary stakeholder committee was divided into work groups. Recommendations for clinical practice and for future research were developed by the committee as an iterative process. This procedure consisted of a literature review phase and focus group interactions with presentations to the entire committee. RESULTS: We first proposed a conceptual framework of disease that describes a series of AKI stages, antecedents and outcomes, and allows a description of research recommendations based on transition between AKI stages. We further proposed methods for testing of the definition and development of research questions to establish the utility of new biomarkers for the diagnosis and staging of AKI and associated illnesses. CONCLUSIONS: Retrospective studies should be conducted to initiate the process of validating the AKIN definition of AKI, followed by comprehensive prospective studies that incorporate sampling for emerging AKI biomarkers.
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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 | 0.113 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.013 | 0.063 |
| Scholarly communication | 0.022 | 0.039 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.010 | 0.009 |
| 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".