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Record W1502398485 · doi:10.1159/000313717

Acute Kidney Injury: Classification and Staging

2010· review· en· W1502398485 on OpenAlex
Dinna N. Cruz, Sean M. Bagshaw, Claudio Ronco, Zaccaria Ricci

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 · 2010
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of Alberta HospitalAlberta Hospital Edmonton
Fundersnot available
KeywordsRifleMedicineAcute kidney injuryIntensive care medicineCreatinineKidney diseaseDiseaseCardiorenal syndromeInternal medicine

Abstract

fetched live from OpenAlex

It was not until recently that consensus definitions for acute kidney injury (AKI) were proposed and published. The RIFLE (Risk-Injury-Failure-Loss-End-stage renal disease) and AKIN (Acute Kidney Injury Network) classifications were designed in order to be easily understood and applied in a variety of clinical and research settings. Their creation was intended to uniformly establish the presence or absence of the AKI and to give a quantitative idea of the severity of the disease unifying the commonly used parameters of serum creatinine and urine output. Subsequent validation showed that both the presence and severity of AKI, defined using RIFLE/AKIN, correlate well with patient outcome. This review will briefly describe the RIFLE/AKIN consensus definitions, its subsequent revisions and its successful validation and application to clinical research. The potential of extending the use of RIFLE/AKIN to the clinical setting of cardiorenal syndromes is also discussed.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.650
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.043
GPT teacher head0.437
Teacher spread0.395 · 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