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Record W2115888632 · doi:10.2215/cjn.04841107

Acute Kidney Injury

2008· article· en· W2115888632 on OpenAlexaffabout
Adeera Levin, John A. Kellum, Ravindra L. Mehta

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2008
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of British ColumbiaProvidence Health Care
Fundersnot available
KeywordsMedicineAcute kidney injuryIntensive care medicineDelphi methodDialysisAntecedent (behavioral psychology)Kidney diseaseEtiologyConceptual frameworkMEDLINEPathologyInternal medicineArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Acute kidney injury (AKI) is widely recognized as an important predictor of morbidity and mortality and as an antecedent to chronic kidney disease (1). In the past 5 yr, there has been increasing interest in understanding this entity, at the both basic science and clinical levels. Recent advances in methodology have led to the description of both serum and urine biomarkers and the imaging of events occurring at the cellular level (2–5). Advances in understanding of and technology associated with dialysis in acute care settings have also contributed to the increasing recognition and treatment of AKI in these settings and the development of research studies to define its appropriate use (6–9). The purpose of this collection of articles is to describe the development of a research agenda, using a modified Delphi approach, that is based on a conceptual framework and a refined definition of AKI. Although each article is written in a different format, the key messages are similar: There is a limited evidence base about most aspects of AKI, and there is a need, through a concerted effort of clinicians and researchers, to address questions that will have an impact on patient outcomes. We acknowledge that AKI is a term that actually encompasses multiple etiologies. For the purposes of defining a research agenda, it is clear that an overarching term is preferred: Specific etiologies can then be more clearly investigated or evaluated within the contextual framework described here. In this edition of CJASN, the first series describe the conceptual model of AKI and how it can be used to answer specific questions, as well as remaining questions regarding the epidemiology of AKI. In particular, we stress the need for better understanding and definitions that are applicable in a multitude of situations. The next article defines the evaluation and early management of AKI, with a major emphasis on the need to distinguish between volume-responsive and volume-unresponsive AKI in a systematic way. The last two articles describe the issues related to renal replacement therapy, particularly indications for and choices of therapy, and again describe key questions related to timing of therapy, defining optimal and minimal dosages of therapy. All of the articles use the definition of AKI recently published (1). The importance of a conceptual model in which to study AKI cannot be overstated. Building on previous work in chronic kidney disease and using the expertise of basic and clinical science, a conceptual model of AKI was developed and refined, initially within one workgroup and subsequently with input from the entire 43 participants at the Vancouver AKIN meeting (September 2007). Briefly, the key aspects of the model include the description of a trajectory of disease from normal populations to at-risk populations to those with early reversible and late nonreversible disease, the ability to define a clear research agenda (both clinical and basic) at every stage of the model, and the concordance with existing accepted models of chronic kidney disease. The second article describes this model in detail. This series of articles serves to focus the community on the importance of AKI as an entity, describe the myriad of possibilities in terms of research and clinical practice opportunities, and describe the current state of knowledge. Through ongoing clinical and research initiatives and leveraging the newly established Acute Kidney Injury Network, we hope to gain an understanding of how best to prevent and treat AKI so that ultimately we are able to improve patient outcomes. The Vancouver conference and the articles in this CJASN selection build on the first AKIN conference held in Amsterdam in 2005 (1). The specific method used is described in the article by Kellum et al. (11) in this series and thus is not repeated in each article. Disclosures None.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.069
GPT teacher head0.424
Teacher spread0.354 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2008
Admission routes2
Has abstractyes

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