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Record W64255601 · doi:10.1159/000349963

Diagnosis of Acute Kidney Injury Using Functional and Injury Biomarkers: Workgroup Statements from the Tenth Acute Dialysis Quality Initiative Consensus Conference

2013· article· en· W64255601 on OpenAlex
Peter A. McCullough, Andrew Shaw, Michael Haase, Josée Bouchard, Sushrut S. Waikar, Edward D. Siew, Patrick Murray, Ravindra L. Mehta, Claudio Ronco

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueContributions to nephrology · 2013
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineAcute kidney injuryRifleIntensive care medicineDialysisContext (archaeology)OliguriaInternal medicineRenal function

Abstract

fetched live from OpenAlex

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.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.067
GPT teacher head0.404
Teacher spread0.337 · 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