Diagnosis of Acute Kidney Injury Using Functional and Injury Biomarkers: Workgroup Statements from the Tenth Acute Dialysis Quality Initiative Consensus Conference
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Bibliographic record
Abstract
Acute kidney injury (AKI) commonly occurs in hospitalized patients and is independently and strongly associates with morbidity and mortality. The clinical benefits of a timely and definitive diagnosis of AKI have not been fully realized due to limitations imposed by the use of serum creatinine and urine output to fulfill diagnostic criteria. These restrictions often lead to diagnostic delays, potential misclassification of actual injury status, and provide little information regarding underlying cause. Novel biomarkers of damage have shown ability to reflect ongoing kidney injury and help further refine existing Risk, Injury, Failure, Loss, End-stage kidney disease (RIFLE) and Acute Kidney Injury Network (AKIN) diagnostic criteria. A comprehensive review of the published literature to date was performed using previously published methodology of the Acute Dialysis Quality Initiative (ADQI) working group to establish consensus statements regarding (i) the overall implementation of injury biomarkers in the concept of AKI diagnosis, (ii) their clinical use, and (iii) future research. On the basis of published data on the ability of novel damage biomarkers to provide diagnostic and prognostic information on AKI, we recommend that novel damage biomarkers may, in the appropriate clinical setting and context (situation consistent with AKI), be used to diagnose AKI even in the absence of changes in serum creatinine or the presence of oliguria as described in the existing RIFLE/AKIN criteria for diagnosis of AKI. Adding injury biomarkers as a criterion for AKI will complement the ability of RIFLE/AKIN to define AKI. Promising diagnostic injury markers include neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule 1 (KIM-1), interleukin 18 (IL-18) and liver-type fatty acid binding protein (L-FABP). However, there are currently insufficient data on damage biomarkers to support their use for AKI staging. Rigorous validation studies measuring the association between the novel damage biomarker(s) and clinically relevant outcomes are needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it